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Name Game

You can find the complete source code for this model here.

The Data set consists of name, gender, state and year with the number of people that were born with that name in that gender, state and year.

query: table('bigquery-public-data.usa_names.usa_1910_2013') -> {
  top: 10
  project: *
}
QUERY RESULTS
state gender year name number
AK F 1,910 Elsie 6
AL F 1,910 Annie 482
AL F 1,910 Katie 84
AL F 1,910 Alma 82
AL F 1,910 Thelma 65
AL F 1,910 Ellen 38
AL F 1,910 Rebecca 35
AL F 1,910 Estella 21
AL F 1,910 Corine 17
AL F 1,910 Margie 17 
[
  {
    "state": "AK",
    "gender": "F",
    "year": 1910,
    "name": "Elsie",
    "number": 6
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Annie",
    "number": 482
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Katie",
    "number": 84
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Alma",
    "number": 82
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Thelma",
    "number": 65
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Ellen",
    "number": 38
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Rebecca",
    "number": 35
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Estella",
    "number": 21
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Corine",
    "number": 17
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Margie",
    "number": 17
  }
]
SELECT 
   base.state as state,
   base.gender as gender,
   base.year as year,
   base.name as name,
   base.number as number
FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
LIMIT 10

The command above says query the table 'bigquery-public-data.usa_names.usa_1910_2013' and project (show) all the columns for the first 10 rows.

Grouping a query

In SQL there are basically two kinds of SELECT commands: SELECT ... GROUP BY and SELECT without a grouping. In malloy, these are two different commands. The command in malloy for SELECT ... GROUP BY is group_by. Since number and year are reserved words, we have to quote the names with back-tics.

query: table('bigquery-public-data.usa_names.usa_1910_2013') -> {
  group_by: name
  aggregate: population is `number`.sum()
}
QUERY RESULTS
name population
James 4,942,431
John 4,834,422
Robert 4,718,787
Michael 4,297,230
William 3,822,209
Mary 3,737,679
David 3,549,801
Richard 2,531,924
Joseph 2,472,917
Charles 2,244,693
Thomas 2,212,781
Christopher 1,987,490
Daniel 1,842,964
Patricia 1,569,570
Matthew 1,538,689
Elizabeth 1,492,404
Jennifer 1,463,145
Linda 1,447,601
Barbara 1,424,203
Donald 1,402,666
Anthony 1,385,453
Paul 1,355,711
Mark 1,339,371
George 1,321,652
Steven 1,269,627
Kenneth 1,259,720
Andrew 1,228,080
Edward 1,210,315
Joshua 1,163,481
Brian 1,156,495
Kevin 1,148,680
Margaret 1,120,766
Susan 1,108,712
Ronald 1,074,141
Timothy 1,055,792
Dorothy 1,052,570
Jessica 1,038,359
Jason 1,009,416
Sarah 1,006,934
Betty 985,364
Nancy 984,185
Karen 982,192
Jeffrey 969,093
Lisa 962,981
Ryan 907,969
Gary 896,637
Helen 895,428
Sandra 870,958
Nicholas 867,231
Eric 862,204
Jacob 848,518
Stephen 844,930
Ashley 841,427
Donna 826,714
Kimberly 826,275
Frank 818,197
Carol 817,290
Jonathan 803,723
Michelle 803,162
Larry 801,398
Emily 779,755
Amanda 769,292
Scott 765,844
Justin 757,273
Laura 745,892
Melissa 745,498
Raymond 744,284
Deborah 735,869
Brandon 735,849
Stephanie 732,651
Rebecca 728,799
Ruth 728,678
Anna 719,803
Sharon 718,701
Kathleen 703,265
Cynthia 703,175
Gregory 701,504
Shirley 686,079
Samuel 681,358
Amy 673,234
Benjamin 664,328
Patrick 655,434
Angela 653,663
Jack 634,113
Virginia 621,448
Jerry 618,105
Dennis 610,894
Catherine 606,029
Brenda 604,067
Alexander 598,944
Katherine 593,220
Pamela 591,307
Nicole 576,870
Tyler 576,031
Christine 574,120
Henry 571,539
Walter 558,026
Peter 553,789
Douglas 552,666
Janet 550,557 
[
  {
    "name": "James",
    "population": 4942431
  },
  {
    "name": "John",
    "population": 4834422
  },
  {
    "name": "Robert",
    "population": 4718787
  },
  {
    "name": "Michael",
    "population": 4297230
  },
  {
    "name": "William",
    "population": 3822209
  },
  {
    "name": "Mary",
    "population": 3737679
  },
  {
    "name": "David",
    "population": 3549801
  },
  {
    "name": "Richard",
    "population": 2531924
  },
  {
    "name": "Joseph",
    "population": 2472917
  },
  {
    "name": "Charles",
    "population": 2244693
  },
  {
    "name": "Thomas",
    "population": 2212781
  },
  {
    "name": "Christopher",
    "population": 1987490
  },
  {
    "name": "Daniel",
    "population": 1842964
  },
  {
    "name": "Patricia",
    "population": 1569570
  },
  {
    "name": "Matthew",
    "population": 1538689
  },
  {
    "name": "Elizabeth",
    "population": 1492404
  },
  {
    "name": "Jennifer",
    "population": 1463145
  },
  {
    "name": "Linda",
    "population": 1447601
  },
  {
    "name": "Barbara",
    "population": 1424203
  },
  {
    "name": "Donald",
    "population": 1402666
  },
  {
    "name": "Anthony",
    "population": 1385453
  },
  {
    "name": "Paul",
    "population": 1355711
  },
  {
    "name": "Mark",
    "population": 1339371
  },
  {
    "name": "George",
    "population": 1321652
  },
  {
    "name": "Steven",
    "population": 1269627
  },
  {
    "name": "Kenneth",
    "population": 1259720
  },
  {
    "name": "Andrew",
    "population": 1228080
  },
  {
    "name": "Edward",
    "population": 1210315
  },
  {
    "name": "Joshua",
    "population": 1163481
  },
  {
    "name": "Brian",
    "population": 1156495
  },
  {
    "name": "Kevin",
    "population": 1148680
  },
  {
    "name": "Margaret",
    "population": 1120766
  },
  {
    "name": "Susan",
    "population": 1108712
  },
  {
    "name": "Ronald",
    "population": 1074141
  },
  {
    "name": "Timothy",
    "population": 1055792
  },
  {
    "name": "Dorothy",
    "population": 1052570
  },
  {
    "name": "Jessica",
    "population": 1038359
  },
  {
    "name": "Jason",
    "population": 1009416
  },
  {
    "name": "Sarah",
    "population": 1006934
  },
  {
    "name": "Betty",
    "population": 985364
  },
  {
    "name": "Nancy",
    "population": 984185
  },
  {
    "name": "Karen",
    "population": 982192
  },
  {
    "name": "Jeffrey",
    "population": 969093
  },
  {
    "name": "Lisa",
    "population": 962981
  },
  {
    "name": "Ryan",
    "population": 907969
  },
  {
    "name": "Gary",
    "population": 896637
  },
  {
    "name": "Helen",
    "population": 895428
  },
  {
    "name": "Sandra",
    "population": 870958
  },
  {
    "name": "Nicholas",
    "population": 867231
  },
  {
    "name": "Eric",
    "population": 862204
  },
  {
    "name": "Jacob",
    "population": 848518
  },
  {
    "name": "Stephen",
    "population": 844930
  },
  {
    "name": "Ashley",
    "population": 841427
  },
  {
    "name": "Donna",
    "population": 826714
  },
  {
    "name": "Kimberly",
    "population": 826275
  },
  {
    "name": "Frank",
    "population": 818197
  },
  {
    "name": "Carol",
    "population": 817290
  },
  {
    "name": "Jonathan",
    "population": 803723
  },
  {
    "name": "Michelle",
    "population": 803162
  },
  {
    "name": "Larry",
    "population": 801398
  },
  {
    "name": "Emily",
    "population": 779755
  },
  {
    "name": "Amanda",
    "population": 769292
  },
  {
    "name": "Scott",
    "population": 765844
  },
  {
    "name": "Justin",
    "population": 757273
  },
  {
    "name": "Laura",
    "population": 745892
  },
  {
    "name": "Melissa",
    "population": 745498
  },
  {
    "name": "Raymond",
    "population": 744284
  },
  {
    "name": "Deborah",
    "population": 735869
  },
  {
    "name": "Brandon",
    "population": 735849
  },
  {
    "name": "Stephanie",
    "population": 732651
  },
  {
    "name": "Rebecca",
    "population": 728799
  },
  {
    "name": "Ruth",
    "population": 728678
  },
  {
    "name": "Anna",
    "population": 719803
  },
  {
    "name": "Sharon",
    "population": 718701
  },
  {
    "name": "Kathleen",
    "population": 703265
  },
  {
    "name": "Cynthia",
    "population": 703175
  },
  {
    "name": "Gregory",
    "population": 701504
  },
  {
    "name": "Shirley",
    "population": 686079
  },
  {
    "name": "Samuel",
    "population": 681358
  },
  {
    "name": "Amy",
    "population": 673234
  },
  {
    "name": "Benjamin",
    "population": 664328
  },
  {
    "name": "Patrick",
    "population": 655434
  },
  {
    "name": "Angela",
    "population": 653663
  },
  {
    "name": "Jack",
    "population": 634113
  },
  {
    "name": "Virginia",
    "population": 621448
  },
  {
    "name": "Jerry",
    "population": 618105
  },
  {
    "name": "Dennis",
    "population": 610894
  },
  {
    "name": "Catherine",
    "population": 606029
  },
  {
    "name": "Brenda",
    "population": 604067
  },
  {
    "name": "Alexander",
    "population": 598944
  },
  {
    "name": "Katherine",
    "population": 593220
  },
  {
    "name": "Pamela",
    "population": 591307
  },
  {
    "name": "Nicole",
    "population": 576870
  },
  {
    "name": "Tyler",
    "population": 576031
  },
  {
    "name": "Christine",
    "population": 574120
  },
  {
    "name": "Henry",
    "population": 571539
  },
  {
    "name": "Walter",
    "population": 558026
  },
  {
    "name": "Peter",
    "population": 553789
  },
  {
    "name": "Douglas",
    "population": 552666
  },
  {
    "name": "Janet",
    "population": 550557
  }
]
SELECT 
   base.name as name,
   SUM(base.number) as population
FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
GROUP BY 1
ORDER BY 2 desc

malloy compiles to SQL. The SQL query for the above command is.

SELECT
   base.name as name,
   SUM(base.number) as population
FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
GROUP BY 1
ORDER BY 2 desc

Expressions

Expressions work much the same as they do in SQL. We can look at population over decade by using a calculation against the year.

query: table('bigquery-public-data.usa_names.usa_1910_2013') -> {
  top: 10
  group_by: decade is floor(`year` / 10) * 10
  aggregate: population is sum(`number`)
}
QUERY RESULTS
decade population
1,950 37,545,138
1,960 35,405,002
1,990 33,309,594
2,000 33,041,629
1,980 32,349,146
1,970 29,238,092
1,940 27,776,913
1,920 21,375,400
1,930 19,782,373
1,910 13,592,350 
[
  {
    "decade": 1950,
    "population": 37545138
  },
  {
    "decade": 1960,
    "population": 35405002
  },
  {
    "decade": 1990,
    "population": 33309594
  },
  {
    "decade": 2000,
    "population": 33041629
  },
  {
    "decade": 1980,
    "population": 32349146
  },
  {
    "decade": 1970,
    "population": 29238092
  },
  {
    "decade": 1940,
    "population": 27776913
  },
  {
    "decade": 1920,
    "population": 21375400
  },
  {
    "decade": 1930,
    "population": 19782373
  },
  {
    "decade": 1910,
    "population": 13592350
  }
]
SELECT 
   (floor(base.year/10))*10 as decade,
   SUM(base.number) as population
FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
GROUP BY 1
ORDER BY 2 desc
LIMIT 10

Cohorts 

explore: names is from(
  table('bigquery-public-data.usa_names.usa_1910_2013') -> {
    group_by: [
      decade is floor(`year` / 10) * 10
      state
      gender
    ]
    aggregate: cohort_population is `number`.sum()
    nest: by_name is {
      group_by: name
      aggregate: population is `number`.sum()
    }
  }
) {
  measure: births_per_100k is floor(by_name.population.sum() / cohort_population.sum() * 100000)
  measure: total_cohort_population is cohort_population.sum()
  dimension: name is concat(by_name.name, '')
}

query: names -> {
  group_by: [
    state
    gender
  ]
  aggregate: total_cohort_population
  nest: names is {
    where: name : 'Michael' | 'Lloyd' | 'Olivia'
    group_by: name
    aggregate: population is by_name.population.sum()
    group_by: births_per_100k
  }
}
QUERY RESULTS
state gender total_​cohort_​population names
CA M 15,011,405
name population births_​per_​100k
Michael 420,127 2,798
Lloyd 8,380 55
CA F 13,799,158
name population births_​per_​100k
Olivia 33,794 244
Michael 2,428 19
NY M 12,203,961
name population births_​per_​100k
Michael 430,732 3,529
Lloyd 7,568 62
Olivia 5 0
NY F 11,490,175
name population births_​per_​100k
Olivia 23,219 202
Michael 1,761 16
TX M 11,126,246
name population births_​per_​100k
Michael 223,900 2,012
Lloyd 8,461 76
Olivia 6 1
TX F 10,341,113
name population births_​per_​100k
Olivia 25,746 248
Michael 1,420 16
Lloyd 15 1
PA M 8,497,961
name population births_​per_​100k
Michael 266,380 3,134
Lloyd 7,874 92
Olivia 22 2
PA F 8,163,949
name population births_​per_​100k
Olivia 16,292 199
Michael 910 11
IL M 7,845,252
name population births_​per_​100k
Michael 251,741 3,208
Lloyd 7,207 91
IL F 7,333,624
name population births_​per_​100k
Olivia 16,450 224
Michael 989 17
OH M 7,324,810
name population births_​per_​100k
Michael 225,302 3,075
Lloyd 6,755 92
OH F 6,876,716
name population births_​per_​100k
Olivia 16,787 244
Michael 969 18
MI M 6,036,661
name population births_​per_​100k
Michael 194,851 3,227
Lloyd 6,130 101
MI F 5,606,794
name population births_​per_​100k
Olivia 13,575 242
Michael 755 17
FL M 4,910,950
name population births_​per_​100k
Michael 138,882 2,828
Lloyd 2,876 58
FL F 4,366,273
name population births_​per_​100k
Olivia 15,208 348
Michael 691 18
NC M 4,326,810
name population births_​per_​100k
Michael 92,414 2,135
Lloyd 4,367 100
NJ M 4,315,822
name population births_​per_​100k
Michael 165,385 3,832
Lloyd 1,720 41
GA M 4,214,825
name population births_​per_​100k
Michael 87,684 2,080
Lloyd 2,508 59
NC F 4,113,425
name population births_​per_​100k
Olivia 10,671 259
Michael 416 19 
[
  {
    "state": "CA",
    "gender": "M",
    "total_cohort_population": 15011405,
    "names": [
      {
        "name": "Michael",
        "population": 420127,
        "births_per_100k": 2798
      },
      {
        "name": "Lloyd",
        "population": 8380,
        "births_per_100k": 55
      }
    ]
  },
  {
    "state": "CA",
    "gender": "F",
    "total_cohort_population": 13799158,
    "names": [
      {
        "name": "Olivia",
        "population": 33794,
        "births_per_100k": 244
      },
      {
        "name": "Michael",
        "population": 2428,
        "births_per_100k": 19
      }
    ]
  },
  {
    "state": "NY",
    "gender": "M",
    "total_cohort_population": 12203961,
    "names": [
      {
        "name": "Michael",
        "population": 430732,
        "births_per_100k": 3529
      },
      {
        "name": "Lloyd",
        "population": 7568,
        "births_per_100k": 62
      },
      {
        "name": "Olivia",
        "population": 5,
        "births_per_100k": 0
      }
    ]
  },
  {
    "state": "NY",
    "gender": "F",
    "total_cohort_population": 11490175,
    "names": [
      {
        "name": "Olivia",
        "population": 23219,
        "births_per_100k": 202
      },
      {
        "name": "Michael",
        "population": 1761,
        "births_per_100k": 16
      }
    ]
  },
  {
    "state": "TX",
    "gender": "M",
    "total_cohort_population": 11126246,
    "names": [
      {
        "name": "Michael",
        "population": 223900,
        "births_per_100k": 2012
      },
      {
        "name": "Lloyd",
        "population": 8461,
        "births_per_100k": 76
      },
      {
        "name": "Olivia",
        "population": 6,
        "births_per_100k": 1
      }
    ]
  },
  {
    "state": "TX",
    "gender": "F",
    "total_cohort_population": 10341113,
    "names": [
      {
        "name": "Olivia",
        "population": 25746,
        "births_per_100k": 248
      },
      {
        "name": "Michael",
        "population": 1420,
        "births_per_100k": 16
      },
      {
        "name": "Lloyd",
        "population": 15,
        "births_per_100k": 1
      }
    ]
  },
  {
    "state": "PA",
    "gender": "M",
    "total_cohort_population": 8497961,
    "names": [
      {
        "name": "Michael",
        "population": 266380,
        "births_per_100k": 3134
      },
      {
        "name": "Lloyd",
        "population": 7874,
        "births_per_100k": 92
      },
      {
        "name": "Olivia",
        "population": 22,
        "births_per_100k": 2
      }
    ]
  },
  {
    "state": "PA",
    "gender": "F",
    "total_cohort_population": 8163949,
    "names": [
      {
        "name": "Olivia",
        "population": 16292,
        "births_per_100k": 199
      },
      {
        "name": "Michael",
        "population": 910,
        "births_per_100k": 11
      }
    ]
  },
  {
    "state": "IL",
    "gender": "M",
    "total_cohort_population": 7845252,
    "names": [
      {
        "name": "Michael",
        "population": 251741,
        "births_per_100k": 3208
      },
      {
        "name": "Lloyd",
        "population": 7207,
        "births_per_100k": 91
      }
    ]
  },
  {
    "state": "IL",
    "gender": "F",
    "total_cohort_population": 7333624,
    "names": [
      {
        "name": "Olivia",
        "population": 16450,
        "births_per_100k": 224
      },
      {
        "name": "Michael",
        "population": 989,
        "births_per_100k": 17
      }
    ]
  },
  {
    "state": "OH",
    "gender": "M",
    "total_cohort_population": 7324810,
    "names": [
      {
        "name": "Michael",
        "population": 225302,
        "births_per_100k": 3075
      },
      {
        "name": "Lloyd",
        "population": 6755,
        "births_per_100k": 92
      }
    ]
  },
  {
    "state": "OH",
    "gender": "F",
    "total_cohort_population": 6876716,
    "names": [
      {
        "name": "Olivia",
        "population": 16787,
        "births_per_100k": 244
      },
      {
        "name": "Michael",
        "population": 969,
        "births_per_100k": 18
      }
    ]
  },
  {
    "state": "MI",
    "gender": "M",
    "total_cohort_population": 6036661,
    "names": [
      {
        "name": "Michael",
        "population": 194851,
        "births_per_100k": 3227
      },
      {
        "name": "Lloyd",
        "population": 6130,
        "births_per_100k": 101
      }
    ]
  },
  {
    "state": "MI",
    "gender": "F",
    "total_cohort_population": 5606794,
    "names": [
      {
        "name": "Olivia",
        "population": 13575,
        "births_per_100k": 242
      },
      {
        "name": "Michael",
        "population": 755,
        "births_per_100k": 17
      }
    ]
  },
  {
    "state": "FL",
    "gender": "M",
    "total_cohort_population": 4910950,
    "names": [
      {
        "name": "Michael",
        "population": 138882,
        "births_per_100k": 2828
      },
      {
        "name": "Lloyd",
        "population": 2876,
        "births_per_100k": 58
      }
    ]
  },
  {
    "state": "FL",
    "gender": "F",
    "total_cohort_population": 4366273,
    "names": [
      {
        "name": "Olivia",
        "population": 15208,
        "births_per_100k": 348
      },
      {
        "name": "Michael",
        "population": 691,
        "births_per_100k": 18
      }
    ]
  },
  {
    "state": "NC",
    "gender": "M",
    "total_cohort_population": 4326810,
    "names": [
      {
        "name": "Michael",
        "population": 92414,
        "births_per_100k": 2135
      },
      {
        "name": "Lloyd",
        "population": 4367,
        "births_per_100k": 100
      }
    ]
  },
  {
    "state": "NJ",
    "gender": "M",
    "total_cohort_population": 4315822,
    "names": [
      {
        "name": "Michael",
        "population": 165385,
        "births_per_100k": 3832
      },
      {
        "name": "Lloyd",
        "population": 1720,
        "births_per_100k": 41
      }
    ]
  },
  {
    "state": "GA",
    "gender": "M",
    "total_cohort_population": 4214825,
    "names": [
      {
        "name": "Michael",
        "population": 87684,
        "births_per_100k": 2080
      },
      {
        "name": "Lloyd",
        "population": 2508,
        "births_per_100k": 59
      }
    ]
  },
  {
    "state": "NC",
    "gender": "F",
    "total_cohort_population": 4113425,
    "names": [
      {
        "name": "Olivia",
        "population": 10671,
        "births_per_100k": 259
      },
      {
        "name": "Michael",
        "population": 416,
        "births_per_100k": 19
      }
    ]
  }
]
WITH __stage0 AS (
  SELECT
    group_set,
    (floor(base.year/10))*10 as decade__0,
    base.state as state__0,
    base.gender as gender__0,
    CASE WHEN group_set=0 THEN SUM(base.number) END as cohort_population__0,
    CASE WHEN group_set=1 THEN base.name END as name__1,
    CASE WHEN group_set=1 THEN SUM(base.number) END as population__1
  FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,1,1)))
  GROUP BY 1,2,3,4,6
)
, __stage1 AS (
  SELECT
    decade__0 as decade,
    state__0 as state,
    gender__0 as gender,
    ANY_VALUE(CASE WHEN group_set=0 THEN cohort_population__0 END) as cohort_population,
    ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(name__1 as name, population__1 as population) END IGNORE NULLS  ORDER BY  population__1 desc ) as by_name
  FROM __stage0
  GROUP BY 1,2,3
  ORDER BY 4 desc
)
, __stage2 AS (
  SELECT
    group_set,
    names.state as state__0,
    names.gender as gender__0,
    CASE WHEN group_set=0 THEN CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64) END as total_cohort_population__0,
    CASE WHEN group_set=1 THEN concat(by_name_0.name,'') END as name__1,
    CASE WHEN group_set=1 THEN SUM(by_name_0.population) END as population__1,
    CASE WHEN group_set=1 THEN floor((SUM(by_name_0.population)/CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64))*100000) END as births_per_100k__1
  FROM (SELECT GENERATE_UUID() as __distinct_key, * FROM __stage1) as names
  , UNNEST(names.by_name) as by_name_0
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,1,1)))
  WHERE (group_set NOT IN (1) OR (group_set IN (1) AND (concat(by_name_0.name,'')='Michael')or((concat(by_name_0.name,'')='Lloyd')or(concat(by_name_0.name,'')='Olivia'))))
  GROUP BY 1,2,3,5
)
SELECT
  state__0 as state,
  gender__0 as gender,
  ANY_VALUE(CASE WHEN group_set=0 THEN total_cohort_population__0 END) as total_cohort_population,
  ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(name__1 as name, population__1 as population, births_per_100k__1 as births_per_100k) END IGNORE NULLS  ORDER BY  population__1 desc ) as names
FROM __stage2
GROUP BY 1,2
ORDER BY 3 desc

Dashboard 

query: names -> name_dashboard { where: name ~ 'Mich%' }
QUERY RESULTS
name total_​population state_​map decade_​graph
Michael 4,387,274 1,5002,0002,5003,0003,500births_per_100k 1,9201,9401,9601,9802,0002,020decade02,0004,000births_per_100kFemaleMalebaby_gender
Michelle 812,865 4006008001,000births_per_100k 1,9201,9401,9601,9802,0002,020decade05001,0001,5002,000births_per_100kFemaleMalebaby_gender
Michele 221,803 100200300400births_per_100k 1,9201,9401,9601,9802,0002,020decade0200400births_per_100kFemaleMalebaby_gender
Micheal 150,850 50100150200births_per_100k 1,9201,9401,9601,9802,0002,020decade0100200births_per_100kFemaleMalebaby_gender
Michaela 48,407 50100150200births_per_100k 1,9401,9601,9802,0002,020decade050100150births_per_100kFemaleMalebaby_gender
Michel 7,056 10203040births_per_100k 1,9201,9401,9601,9802,000decade02468births_per_100kFemaleMalebaby_gender
Michell 4,377 5101520births_per_100k 1,9601,9802,000decade0510births_per_100kFemaleMalebaby_gender
Michal 2,371 02468births_per_100k 1,9401,9601,9802,0002,020decade01020births_per_100kFemaleMalebaby_gender
Michale 958 1234births_per_100k 1,9601,9802,000decade0123births_per_100kMalebaby_gender
Michelina 903 02468births_per_100k 1,9201,9401,9601,9802,000decade0510births_per_100kFemalebaby_gender
Michaella 705 123456births_per_100k 1,9601,9701,9801,9902,0002,010decade01234births_per_100kFemalebaby_gender
Michela 616 24681012births_per_100k 1,9401,9601,9802,000decade024births_per_100kFemalebaby_gender
Michaelangelo 574 0123births_per_100k 1,9701,9801,9902,0002,0102,020decade0123births_per_100kMalebaby_gender
Michaelene 477 01234births_per_100k 1,9301,9401,9501,9601,9701,980decade01234births_per_100kFemalebaby_gender
Michala 465 02468births_per_100k 1,9701,9801,9902,000decade024births_per_100kFemalebaby_gender 
[
  {
    "name": "Michael",
    "total_population": 4387274,
    "state_map": [
      {
        "state": "RI",
        "births_per_100k": 3702
      },
      {
        "state": "AK",
        "births_per_100k": 3331
      },
      {
        "state": "DE",
        "births_per_100k": 3328
      },
      {
        "state": "VT",
        "births_per_100k": 3049
      },
      {
        "state": "NH",
        "births_per_100k": 2813
      },
      {
        "state": "ME",
        "births_per_100k": 2804
      },
      {
        "state": "WY",
        "births_per_100k": 2667
      },
      {
        "state": "HI",
        "births_per_100k": 2529
      },
      {
        "state": "CT",
        "births_per_100k": 2489
      },
      {
        "state": "NV",
        "births_per_100k": 2443
      },
      {
        "state": "DC",
        "births_per_100k": 2347
      },
      {
        "state": "MT",
        "births_per_100k": 2292
      },
      {
        "state": "MA",
        "births_per_100k": 2240
      },
      {
        "state": "NJ",
        "births_per_100k": 2232
      },
      {
        "state": "SD",
        "births_per_100k": 2228
      },
      {
        "state": "ND",
        "births_per_100k": 1975
      },
      {
        "state": "MD",
        "births_per_100k": 1967
      },
      {
        "state": "ID",
        "births_per_100k": 1964
      },
      {
        "state": "WI",
        "births_per_100k": 1918
      },
      {
        "state": "NE",
        "births_per_100k": 1917
      },
      {
        "state": "OR",
        "births_per_100k": 1873
      },
      {
        "state": "IL",
        "births_per_100k": 1854
      },
      {
        "state": "MI",
        "births_per_100k": 1837
      },
      {
        "state": "NY",
        "births_per_100k": 1817
      },
      {
        "state": "WV",
        "births_per_100k": 1796
      },
      {
        "state": "IA",
        "births_per_100k": 1772
      },
      {
        "state": "OH",
        "births_per_100k": 1763
      },
      {
        "state": "CO",
        "births_per_100k": 1747
      },
      {
        "state": "NM",
        "births_per_100k": 1733
      },
      {
        "state": "MN",
        "births_per_100k": 1701
      },
      {
        "state": "IN",
        "births_per_100k": 1690
      },
      {
        "state": "WA",
        "births_per_100k": 1680
      },
      {
        "state": "MO",
        "births_per_100k": 1659
      },
      {
        "state": "VA",
        "births_per_100k": 1657
      },
      {
        "state": "UT",
        "births_per_100k": 1611
      },
      {
        "state": "AZ",
        "births_per_100k": 1610
      },
      {
        "state": "PA",
        "births_per_100k": 1609
      },
      {
        "state": "KS",
        "births_per_100k": 1604
      },
      {
        "state": "FL",
        "births_per_100k": 1564
      },
      {
        "state": "OK",
        "births_per_100k": 1545
      },
      {
        "state": "LA",
        "births_per_100k": 1433
      },
      {
        "state": "NC",
        "births_per_100k": 1405
      },
      {
        "state": "CA",
        "births_per_100k": 1398
      },
      {
        "state": "AR",
        "births_per_100k": 1385
      },
      {
        "state": "TN",
        "births_per_100k": 1370
      },
      {
        "state": "SC",
        "births_per_100k": 1367
      },
      {
        "state": "KY",
        "births_per_100k": 1348
      },
      {
        "state": "AL",
        "births_per_100k": 1244
      },
      {
        "state": "MS",
        "births_per_100k": 1214
      },
      {
        "state": "GA",
        "births_per_100k": 1144
      },
      {
        "state": "TX",
        "births_per_100k": 1028
      }
    ],
    "decade_graph": [
      {
        "decade": 1970,
        "births_per_100k": 4562,
        "baby_gender": "Male"
      },
      {
        "decade": 1960,
        "births_per_100k": 4510,
        "baby_gender": "Male"
      },
      {
        "decade": 1950,
        "births_per_100k": 4301,
        "baby_gender": "Male"
      },
      {
        "decade": 1980,
        "births_per_100k": 3848,
        "baby_gender": "Male"
      },
      {
        "decade": 1990,
        "births_per_100k": 2587,
        "baby_gender": "Male"
      },
      {
        "decade": 1940,
        "births_per_100k": 2367,
        "baby_gender": "Male"
      },
      {
        "decade": 2000,
        "births_per_100k": 1409,
        "baby_gender": "Male"
      },
      {
        "decade": 2010,
        "births_per_100k": 883,
        "baby_gender": "Male"
      },
      {
        "decade": 2020,
        "births_per_100k": 661,
        "baby_gender": "Male"
      },
      {
        "decade": 1930,
        "births_per_100k": 564,
        "baby_gender": "Male"
      },
      {
        "decade": 1910,
        "births_per_100k": 501,
        "baby_gender": "Male"
      },
      {
        "decade": 1920,
        "births_per_100k": 366,
        "baby_gender": "Male"
      },
      {
        "decade": 1970,
        "births_per_100k": 35,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 32,
        "baby_gender": "Female"
      },
      {
        "decade": 1960,
        "births_per_100k": 19,
        "baby_gender": "Female"
      },
      {
        "decade": 1950,
        "births_per_100k": 10,
        "baby_gender": "Female"
      },
      {
        "decade": 1990,
        "births_per_100k": 10,
        "baby_gender": "Female"
      },
      {
        "decade": 1940,
        "births_per_100k": 8,
        "baby_gender": "Female"
      },
      {
        "decade": 1930,
        "births_per_100k": 4,
        "baby_gender": "Female"
      },
      {
        "decade": 1920,
        "births_per_100k": 4,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 3,
        "baby_gender": "Female"
      },
      {
        "decade": 2010,
        "births_per_100k": 0,
        "baby_gender": "Female"
      },
      {
        "decade": 1910,
        "births_per_100k": 0,
        "baby_gender": "Female"
      }
    ]
  },
  {
    "name": "Michelle",
    "total_population": 812865,
    "state_map": [
      {
        "state": "VT",
        "births_per_100k": 1137
      },
      {
        "state": "AK",
        "births_per_100k": 1018
      },
      {
        "state": "HI",
        "births_per_100k": 992
      },
      {
        "state": "WY",
        "births_per_100k": 992
      },
      {
        "state": "DE",
        "births_per_100k": 920
      },
      {
        "state": "RI",
        "births_per_100k": 909
      },
      {
        "state": "ME",
        "births_per_100k": 889
      },
      {
        "state": "NH",
        "births_per_100k": 878
      },
      {
        "state": "NE",
        "births_per_100k": 848
      },
      {
        "state": "SD",
        "births_per_100k": 834
      },
      {
        "state": "ND",
        "births_per_100k": 826
      },
      {
        "state": "NM",
        "births_per_100k": 784
      },
      {
        "state": "CO",
        "births_per_100k": 772
      },
      {
        "state": "NV",
        "births_per_100k": 771
      },
      {
        "state": "MT",
        "births_per_100k": 768
      },
      {
        "state": "IA",
        "births_per_100k": 761
      },
      {
        "state": "KS",
        "births_per_100k": 754
      },
      {
        "state": "CT",
        "births_per_100k": 752
      },
      {
        "state": "OR",
        "births_per_100k": 744
      },
      {
        "state": "UT",
        "births_per_100k": 731
      },
      {
        "state": "DC",
        "births_per_100k": 725
      },
      {
        "state": "WA",
        "births_per_100k": 713
      },
      {
        "state": "ID",
        "births_per_100k": 695
      },
      {
        "state": "AZ",
        "births_per_100k": 694
      },
      {
        "state": "MO",
        "births_per_100k": 665
      },
      {
        "state": "MA",
        "births_per_100k": 652
      },
      {
        "state": "IN",
        "births_per_100k": 611
      },
      {
        "state": "MN",
        "births_per_100k": 593
      },
      {
        "state": "MD",
        "births_per_100k": 583
      },
      {
        "state": "MI",
        "births_per_100k": 563
      },
      {
        "state": "WI",
        "births_per_100k": 531
      },
      {
        "state": "OH",
        "births_per_100k": 524
      },
      {
        "state": "OK",
        "births_per_100k": 507
      },
      {
        "state": "NJ",
        "births_per_100k": 504
      },
      {
        "state": "LA",
        "births_per_100k": 502
      },
      {
        "state": "VA",
        "births_per_100k": 490
      },
      {
        "state": "IL",
        "births_per_100k": 468
      },
      {
        "state": "FL",
        "births_per_100k": 463
      },
      {
        "state": "PA",
        "births_per_100k": 458
      },
      {
        "state": "CA",
        "births_per_100k": 449
      },
      {
        "state": "WV",
        "births_per_100k": 437
      },
      {
        "state": "AR",
        "births_per_100k": 425
      },
      {
        "state": "NY",
        "births_per_100k": 416
      },
      {
        "state": "SC",
        "births_per_100k": 406
      },
      {
        "state": "KY",
        "births_per_100k": 365
      },
      {
        "state": "TN",
        "births_per_100k": 327
      },
      {
        "state": "GA",
        "births_per_100k": 318
      },
      {
        "state": "TX",
        "births_per_100k": 310
      },
      {
        "state": "MS",
        "births_per_100k": 298
      },
      {
        "state": "NC",
        "births_per_100k": 285
      },
      {
        "state": "AL",
        "births_per_100k": 281
      }
    ],
    "decade_graph": [
      {
        "decade": 1970,
        "births_per_100k": 1814,
        "baby_gender": "Female"
      },
      {
        "decade": 1960,
        "births_per_100k": 1230,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 1018,
        "baby_gender": "Female"
      },
      {
        "decade": 1990,
        "births_per_100k": 559,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 283,
        "baby_gender": "Female"
      },
      {
        "decade": 1950,
        "births_per_100k": 258,
        "baby_gender": "Female"
      },
      {
        "decade": 2010,
        "births_per_100k": 120,
        "baby_gender": "Female"
      },
      {
        "decade": 2020,
        "births_per_100k": 74,
        "baby_gender": "Female"
      },
      {
        "decade": 1940,
        "births_per_100k": 40,
        "baby_gender": "Female"
      },
      {
        "decade": 1970,
        "births_per_100k": 5,
        "baby_gender": "Male"
      },
      {
        "decade": 1930,
        "births_per_100k": 4,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 4,
        "baby_gender": "Male"
      },
      {
        "decade": 1990,
        "births_per_100k": 3,
        "baby_gender": "Male"
      },
      {
        "decade": 1960,
        "births_per_100k": 2,
        "baby_gender": "Male"
      },
      {
        "decade": 1920,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 0,
        "baby_gender": "Male"
      }
    ]
  },
  {
    "name": "Michele",
    "total_population": 221803,
    "state_map": [
      {
        "state": "DE",
        "births_per_100k": 472
      },
      {
        "state": "CT",
        "births_per_100k": 448
      },
      {
        "state": "HI",
        "births_per_100k": 368
      },
      {
        "state": "VT",
        "births_per_100k": 351
      },
      {
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      },
      {
        "state": "NV",
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      },
      {
        "state": "MT",
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      },
      {
        "state": "ND",
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      {
        "state": "PA",
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      },
      {
        "state": "MN",
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      },
      {
        "state": "UT",
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      },
      {
        "state": "MI",
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      },
      {
        "state": "IN",
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      },
      {
        "state": "OR",
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        "state": "MA",
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        "state": "ID",
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      {
        "state": "NY",
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        "state": "IL",
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      {
        "state": "NM",
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      {
        "state": "VA",
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      {
        "state": "AZ",
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      },
      {
        "state": "KY",
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        "state": "LA",
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      },
      {
        "state": "OK",
        "births_per_100k": 106
      },
      {
        "state": "FL",
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      },
      {
        "state": "CA",
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      },
      {
        "state": "AR",
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      {
        "state": "SC",
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      },
      {
        "state": "AL",
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      },
      {
        "state": "GA",
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      },
      {
        "state": "TX",
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      },
      {
        "state": "NC",
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      },
      {
        "state": "MS",
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      },
      {
        "state": "TN",
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    ],
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        "decade": 1960,
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        "baby_gender": "Female"
      },
      {
        "decade": 1970,
        "births_per_100k": 411,
        "baby_gender": "Female"
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        "decade": 1950,
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      },
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        "decade": 1980,
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        "baby_gender": "Female"
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      {
        "decade": 1940,
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        "baby_gender": "Female"
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      {
        "decade": 1990,
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        "baby_gender": "Female"
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      {
        "decade": 2000,
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        "baby_gender": "Female"
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      {
        "decade": 2020,
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        "baby_gender": "Female"
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        "decade": 1910,
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        "baby_gender": "Male"
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        "decade": 1970,
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      {
        "decade": 1930,
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        "baby_gender": "Female"
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        "decade": 1980,
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        "baby_gender": "Male"
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      {
        "decade": 2010,
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        "baby_gender": "Female"
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      {
        "decade": 1960,
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        "baby_gender": "Male"
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      {
        "decade": 1920,
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        "baby_gender": "Female"
      },
      {
        "decade": 1990,
        "births_per_100k": 1,
        "baby_gender": "Male"
      },
      {
        "decade": 1950,
        "births_per_100k": 0,
        "baby_gender": "Male"
      },
      {
        "decade": 1930,
        "births_per_100k": 0,
        "baby_gender": "Male"
      },
      {
        "decade": 1940,
        "births_per_100k": 0,
        "baby_gender": "Male"
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    ]
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    "name": "Micheal",
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        "state": "MS",
        "births_per_100k": 222
      },
      {
        "state": "SD",
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        "state": "OK",
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        "state": "OR",
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        "state": "NE",
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        "state": "KY",
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        "state": "GA",
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      {
        "state": "IN",
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        "state": "MI",
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      {
        "state": "CO",
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      {
        "state": "VA",
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      {
        "state": "NV",
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        "state": "MD",
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      {
        "state": "MN",
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      {
        "state": "AZ",
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      {
        "state": "FL",
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      {
        "state": "WI",
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        "state": "ME",
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      },
      {
        "state": "IL",
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        "state": "VT",
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      },
      {
        "state": "OH",
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      {
        "state": "TX",
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      {
        "state": "NH",
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      {
        "state": "DE",
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      },
      {
        "state": "CA",
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      },
      {
        "state": "PA",
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      },
      {
        "state": "CT",
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        "state": "NY",
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      {
        "state": "RI",
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      {
        "state": "NJ",
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      {
        "state": "MA",
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      },
      {
        "state": "HI",
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    ],
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        "decade": 1950,
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        "decade": 1960,
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        "decade": 2000,
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        "decade": 1920,
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        "decade": 2010,
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      {
        "decade": 2020,
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        "baby_gender": "Male"
      },
      {
        "decade": 1930,
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      {
        "decade": 2000,
        "births_per_100k": 3,
        "baby_gender": "Female"
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        "decade": 1970,
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      {
        "decade": 1960,
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        "baby_gender": "Female"
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      {
        "decade": 1980,
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      {
        "decade": 1990,
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        "baby_gender": "Female"
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        "baby_gender": "Female"
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    ]
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    "name": "Michaela",
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        "state": "WV",
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      {
        "state": "WI",
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      {
        "state": "WA",
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      {
        "state": "NC",
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      {
        "state": "OR",
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        "state": "VA",
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        "state": "IN",
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        "state": "ID",
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        "state": "CO",
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        "state": "AZ",
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        "state": "GA",
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      },
      {
        "state": "NV",
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      },
      {
        "state": "DC",
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        "state": "FL",
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        "state": "LA",
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      {
        "state": "PA",
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        "state": "NJ",
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      },
      {
        "state": "MI",
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      },
      {
        "state": "TX",
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      {
        "state": "NY",
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      },
      {
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      }
    ],
    "decade_graph": [
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        "decade": 2000,
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      },
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        "decade": 2020,
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      {
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      {
        "decade": 1940,
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        "decade": 1990,
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    ]
  },
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      },
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      },
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        "state": "VT",
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        "state": "LA",
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        "state": "IN",
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      {
        "state": "AR",
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      },
      {
        "state": "IA",
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        "state": "FL",
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        "state": "ID",
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      },
      {
        "state": "NY",
        "births_per_100k": 5
      },
      {
        "state": "CA",
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      },
      {
        "state": "TN",
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      },
      {
        "state": "KY",
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      },
      {
        "state": "CO",
        "births_per_100k": 5
      },
      {
        "state": "WA",
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      },
      {
        "state": "MI",
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      },
      {
        "state": "OH",
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      },
      {
        "state": "OR",
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      {
        "state": "VA",
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      },
      {
        "state": "SC",
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      },
      {
        "state": "TX",
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      },
      {
        "state": "GA",
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      },
      {
        "state": "IL",
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      },
      {
        "state": "KS",
        "births_per_100k": 3
      },
      {
        "state": "AL",
        "births_per_100k": 3
      },
      {
        "state": "NC",
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      },
      {
        "state": "CT",
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      },
      {
        "state": "MD",
        "births_per_100k": 2
      },
      {
        "state": "MN",
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      },
      {
        "state": "WI",
        "births_per_100k": 2
      },
      {
        "state": "NJ",
        "births_per_100k": 2
      },
      {
        "state": "PA",
        "births_per_100k": 1
      },
      {
        "state": "AZ",
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      }
    ],
    "decade_graph": [
      {
        "decade": 1950,
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        "baby_gender": "Male"
      },
      {
        "decade": 1960,
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        "baby_gender": "Male"
      },
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        "decade": 1970,
        "births_per_100k": 6,
        "baby_gender": "Male"
      },
      {
        "decade": 1980,
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        "baby_gender": "Male"
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      {
        "decade": 1940,
        "births_per_100k": 4,
        "baby_gender": "Male"
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        "decade": 1990,
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        "baby_gender": "Male"
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        "decade": 1910,
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        "baby_gender": "Male"
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      {
        "decade": 1960,
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        "baby_gender": "Female"
      },
      {
        "decade": 1970,
        "births_per_100k": 3,
        "baby_gender": "Female"
      },
      {
        "decade": 1930,
        "births_per_100k": 3,
        "baby_gender": "Male"
      },
      {
        "decade": 2000,
        "births_per_100k": 3,
        "baby_gender": "Female"
      },
      {
        "decade": 1920,
        "births_per_100k": 3,
        "baby_gender": "Male"
      },
      {
        "decade": 1990,
        "births_per_100k": 3,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 3,
        "baby_gender": "Male"
      },
      {
        "decade": 1940,
        "births_per_100k": 2,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 2,
        "baby_gender": "Female"
      },
      {
        "decade": 1950,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 2010,
        "births_per_100k": 1,
        "baby_gender": "Male"
      },
      {
        "decade": 2010,
        "births_per_100k": 0,
        "baby_gender": "Female"
      }
    ]
  },
  {
    "name": "Michell",
    "total_population": 4377,
    "state_map": [
      {
        "state": "MS",
        "births_per_100k": 21
      },
      {
        "state": "LA",
        "births_per_100k": 16
      },
      {
        "state": "MO",
        "births_per_100k": 14
      },
      {
        "state": "AR",
        "births_per_100k": 13
      },
      {
        "state": "NV",
        "births_per_100k": 10
      },
      {
        "state": "CO",
        "births_per_100k": 10
      },
      {
        "state": "OK",
        "births_per_100k": 9
      },
      {
        "state": "KS",
        "births_per_100k": 9
      },
      {
        "state": "IN",
        "births_per_100k": 9
      },
      {
        "state": "GA",
        "births_per_100k": 9
      },
      {
        "state": "KY",
        "births_per_100k": 9
      },
      {
        "state": "AL",
        "births_per_100k": 9
      },
      {
        "state": "DC",
        "births_per_100k": 8
      },
      {
        "state": "SC",
        "births_per_100k": 8
      },
      {
        "state": "AZ",
        "births_per_100k": 7
      },
      {
        "state": "CA",
        "births_per_100k": 6
      },
      {
        "state": "OH",
        "births_per_100k": 6
      },
      {
        "state": "TX",
        "births_per_100k": 6
      },
      {
        "state": "FL",
        "births_per_100k": 6
      },
      {
        "state": "WA",
        "births_per_100k": 6
      },
      {
        "state": "NY",
        "births_per_100k": 5
      },
      {
        "state": "IL",
        "births_per_100k": 5
      },
      {
        "state": "MI",
        "births_per_100k": 5
      },
      {
        "state": "MD",
        "births_per_100k": 5
      },
      {
        "state": "NJ",
        "births_per_100k": 4
      },
      {
        "state": "WI",
        "births_per_100k": 4
      },
      {
        "state": "PA",
        "births_per_100k": 4
      },
      {
        "state": "NC",
        "births_per_100k": 4
      },
      {
        "state": "MN",
        "births_per_100k": 3
      },
      {
        "state": "TN",
        "births_per_100k": 3
      },
      {
        "state": "OR",
        "births_per_100k": 3
      },
      {
        "state": "MA",
        "births_per_100k": 2
      },
      {
        "state": "VA",
        "births_per_100k": 2
      },
      {
        "state": "IA",
        "births_per_100k": 2
      }
    ],
    "decade_graph": [
      {
        "decade": 1970,
        "births_per_100k": 13,
        "baby_gender": "Female"
      },
      {
        "decade": 1960,
        "births_per_100k": 7,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 6,
        "baby_gender": "Female"
      },
      {
        "decade": 1990,
        "births_per_100k": 6,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 5,
        "baby_gender": "Female"
      },
      {
        "decade": 2010,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 1950,
        "births_per_100k": 0,
        "baby_gender": "Female"
      },
      {
        "decade": 1970,
        "births_per_100k": 0,
        "baby_gender": "Male"
      },
      {
        "decade": 1990,
        "births_per_100k": 0,
        "baby_gender": "Male"
      }
    ]
  },
  {
    "name": "Michal",
    "total_population": 2371,
    "state_map": [
      {
        "state": "UT",
        "births_per_100k": 8
      },
      {
        "state": "IL",
        "births_per_100k": 8
      },
      {
        "state": "NJ",
        "births_per_100k": 6
      },
      {
        "state": "NY",
        "births_per_100k": 5
      },
      {
        "state": "CT",
        "births_per_100k": 5
      },
      {
        "state": "MD",
        "births_per_100k": 2
      },
      {
        "state": "IA",
        "births_per_100k": 2
      },
      {
        "state": "CO",
        "births_per_100k": 2
      },
      {
        "state": "CA",
        "births_per_100k": 1
      },
      {
        "state": "PA",
        "births_per_100k": 1
      },
      {
        "state": "TX",
        "births_per_100k": 1
      },
      {
        "state": "FL",
        "births_per_100k": 1
      },
      {
        "state": "OH",
        "births_per_100k": 1
      },
      {
        "state": "MO",
        "births_per_100k": 1
      },
      {
        "state": "MA",
        "births_per_100k": 1
      },
      {
        "state": "TN",
        "births_per_100k": 1
      },
      {
        "state": "GA",
        "births_per_100k": 1
      },
      {
        "state": "AL",
        "births_per_100k": 1
      },
      {
        "state": "MI",
        "births_per_100k": 0
      }
    ],
    "decade_graph": [
      {
        "decade": 2020,
        "births_per_100k": 22,
        "baby_gender": "Female"
      },
      {
        "decade": 2010,
        "births_per_100k": 6,
        "baby_gender": "Male"
      },
      {
        "decade": 2000,
        "births_per_100k": 5,
        "baby_gender": "Male"
      },
      {
        "decade": 1980,
        "births_per_100k": 4,
        "baby_gender": "Female"
      },
      {
        "decade": 1990,
        "births_per_100k": 4,
        "baby_gender": "Male"
      },
      {
        "decade": 2010,
        "births_per_100k": 4,
        "baby_gender": "Female"
      },
      {
        "decade": 1990,
        "births_per_100k": 3,
        "baby_gender": "Female"
      },
      {
        "decade": 1970,
        "births_per_100k": 3,
        "baby_gender": "Female"
      },
      {
        "decade": 1940,
        "births_per_100k": 2,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 2,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 1,
        "baby_gender": "Male"
      },
      {
        "decade": 1950,
        "births_per_100k": 1,
        "baby_gender": "Male"
      },
      {
        "decade": 1940,
        "births_per_100k": 0,
        "baby_gender": "Male"
      },
      {
        "decade": 1950,
        "births_per_100k": 0,
        "baby_gender": "Female"
      },
      {
        "decade": 1970,
        "births_per_100k": 0,
        "baby_gender": "Male"
      },
      {
        "decade": 1960,
        "births_per_100k": 0,
        "baby_gender": "Female"
      },
      {
        "decade": 1960,
        "births_per_100k": 0,
        "baby_gender": "Male"
      }
    ]
  },
  {
    "name": "Michale",
    "total_population": 958,
    "state_map": [
      {
        "state": "UT",
        "births_per_100k": 4
      },
      {
        "state": "CA",
        "births_per_100k": 3
      },
      {
        "state": "FL",
        "births_per_100k": 2
      },
      {
        "state": "IL",
        "births_per_100k": 2
      },
      {
        "state": "TX",
        "births_per_100k": 2
      },
      {
        "state": "OK",
        "births_per_100k": 2
      },
      {
        "state": "AZ",
        "births_per_100k": 1
      },
      {
        "state": "MI",
        "births_per_100k": 1
      },
      {
        "state": "GA",
        "births_per_100k": 1
      },
      {
        "state": "NY",
        "births_per_100k": 1
      },
      {
        "state": "IN",
        "births_per_100k": 1
      },
      {
        "state": "VA",
        "births_per_100k": 1
      },
      {
        "state": "NC",
        "births_per_100k": 1
      },
      {
        "state": "OH",
        "births_per_100k": 1
      },
      {
        "state": "PA",
        "births_per_100k": 1
      },
      {
        "state": "KY",
        "births_per_100k": 1
      },
      {
        "state": "TN",
        "births_per_100k": 1
      }
    ],
    "decade_graph": [
      {
        "decade": 1970,
        "births_per_100k": 3,
        "baby_gender": "Male"
      },
      {
        "decade": 1980,
        "births_per_100k": 3,
        "baby_gender": "Male"
      },
      {
        "decade": 1950,
        "births_per_100k": 1,
        "baby_gender": "Male"
      },
      {
        "decade": 1960,
        "births_per_100k": 1,
        "baby_gender": "Male"
      },
      {
        "decade": 2010,
        "births_per_100k": 1,
        "baby_gender": "Male"
      },
      {
        "decade": 1990,
        "births_per_100k": 0,
        "baby_gender": "Male"
      }
    ]
  },
  {
    "name": "Michelina",
    "total_population": 903,
    "state_map": [
      {
        "state": "CT",
        "births_per_100k": 9
      },
      {
        "state": "RI",
        "births_per_100k": 9
      },
      {
        "state": "MA",
        "births_per_100k": 8
      },
      {
        "state": "NJ",
        "births_per_100k": 7
      },
      {
        "state": "NY",
        "births_per_100k": 6
      },
      {
        "state": "PA",
        "births_per_100k": 1
      },
      {
        "state": "IL",
        "births_per_100k": 0
      }
    ],
    "decade_graph": [
      {
        "decade": 1920,
        "births_per_100k": 12,
        "baby_gender": "Female"
      },
      {
        "decade": 1910,
        "births_per_100k": 10,
        "baby_gender": "Female"
      },
      {
        "decade": 1930,
        "births_per_100k": 8,
        "baby_gender": "Female"
      },
      {
        "decade": 1940,
        "births_per_100k": 3,
        "baby_gender": "Female"
      },
      {
        "decade": 1950,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 1960,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 1970,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 2010,
        "births_per_100k": 0,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 0,
        "baby_gender": "Female"
      },
      {
        "decade": 1990,
        "births_per_100k": 0,
        "baby_gender": "Female"
      }
    ]
  },
  {
    "name": "Michaella",
    "total_population": 705,
    "state_map": [
      {
        "state": "MA",
        "births_per_100k": 6
      },
      {
        "state": "MO",
        "births_per_100k": 6
      },
      {
        "state": "WA",
        "births_per_100k": 5
      },
      {
        "state": "FL",
        "births_per_100k": 4
      },
      {
        "state": "UT",
        "births_per_100k": 3
      },
      {
        "state": "NC",
        "births_per_100k": 3
      },
      {
        "state": "OH",
        "births_per_100k": 3
      },
      {
        "state": "NY",
        "births_per_100k": 3
      },
      {
        "state": "CO",
        "births_per_100k": 3
      },
      {
        "state": "IN",
        "births_per_100k": 3
      },
      {
        "state": "PA",
        "births_per_100k": 2
      },
      {
        "state": "IL",
        "births_per_100k": 2
      },
      {
        "state": "NJ",
        "births_per_100k": 2
      },
      {
        "state": "MI",
        "births_per_100k": 2
      },
      {
        "state": "GA",
        "births_per_100k": 1
      },
      {
        "state": "CA",
        "births_per_100k": 1
      },
      {
        "state": "TX",
        "births_per_100k": 1
      }
    ],
    "decade_graph": [
      {
        "decade": 1990,
        "births_per_100k": 4,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 2010,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 0,
        "baby_gender": "Female"
      },
      {
        "decade": 1960,
        "births_per_100k": 0,
        "baby_gender": "Female"
      }
    ]
  },
  {
    "name": "Michela",
    "total_population": 616,
    "state_map": [
      {
        "state": "MA",
        "births_per_100k": 12
      },
      {
        "state": "NY",
        "births_per_100k": 3
      },
      {
        "state": "NJ",
        "births_per_100k": 3
      },
      {
        "state": "PA",
        "births_per_100k": 2
      },
      {
        "state": "CA",
        "births_per_100k": 2
      },
      {
        "state": "FL",
        "births_per_100k": 1
      },
      {
        "state": "IL",
        "births_per_100k": 1
      },
      {
        "state": "OH",
        "births_per_100k": 1
      }
    ],
    "decade_graph": [
      {
        "decade": 1990,
        "births_per_100k": 5,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 2,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 2010,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 1970,
        "births_per_100k": 0,
        "baby_gender": "Female"
      },
      {
        "decade": 1940,
        "births_per_100k": 0,
        "baby_gender": "Female"
      }
    ]
  },
  {
    "name": "Michaelangelo",
    "total_population": 574,
    "state_map": [
      {
        "state": "CA",
        "births_per_100k": 3
      },
      {
        "state": "AZ",
        "births_per_100k": 1
      },
      {
        "state": "NY",
        "births_per_100k": 1
      },
      {
        "state": "FL",
        "births_per_100k": 1
      },
      {
        "state": "TX",
        "births_per_100k": 1
      },
      {
        "state": "MA",
        "births_per_100k": 1
      },
      {
        "state": "IL",
        "births_per_100k": 0
      }
    ],
    "decade_graph": [
      {
        "decade": 2020,
        "births_per_100k": 3,
        "baby_gender": "Male"
      },
      {
        "decade": 2000,
        "births_per_100k": 2,
        "baby_gender": "Male"
      },
      {
        "decade": 1990,
        "births_per_100k": 2,
        "baby_gender": "Male"
      },
      {
        "decade": 2010,
        "births_per_100k": 2,
        "baby_gender": "Male"
      },
      {
        "decade": 1980,
        "births_per_100k": 1,
        "baby_gender": "Male"
      },
      {
        "decade": 1970,
        "births_per_100k": 0,
        "baby_gender": "Male"
      }
    ]
  },
  {
    "name": "Michaelene",
    "total_population": 477,
    "state_map": [
      {
        "state": "PA",
        "births_per_100k": 4
      },
      {
        "state": "IL",
        "births_per_100k": 4
      },
      {
        "state": "IA",
        "births_per_100k": 2
      },
      {
        "state": "MI",
        "births_per_100k": 1
      },
      {
        "state": "IN",
        "births_per_100k": 1
      },
      {
        "state": "OH",
        "births_per_100k": 1
      },
      {
        "state": "NJ",
        "births_per_100k": 1
      },
      {
        "state": "NY",
        "births_per_100k": 0
      }
    ],
    "decade_graph": [
      {
        "decade": 1940,
        "births_per_100k": 4,
        "baby_gender": "Female"
      },
      {
        "decade": 1960,
        "births_per_100k": 3,
        "baby_gender": "Female"
      },
      {
        "decade": 1950,
        "births_per_100k": 2,
        "baby_gender": "Female"
      },
      {
        "decade": 1930,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 1970,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 0,
        "baby_gender": "Female"
      }
    ]
  },
  {
    "name": "Michala",
    "total_population": 465,
    "state_map": [
      {
        "state": "NC",
        "births_per_100k": 9
      },
      {
        "state": "OK",
        "births_per_100k": 7
      },
      {
        "state": "NE",
        "births_per_100k": 5
      },
      {
        "state": "VA",
        "births_per_100k": 5
      },
      {
        "state": "MO",
        "births_per_100k": 5
      },
      {
        "state": "TX",
        "births_per_100k": 4
      },
      {
        "state": "OH",
        "births_per_100k": 4
      },
      {
        "state": "MI",
        "births_per_100k": 4
      },
      {
        "state": "PA",
        "births_per_100k": 3
      },
      {
        "state": "GA",
        "births_per_100k": 3
      },
      {
        "state": "LA",
        "births_per_100k": 2
      },
      {
        "state": "TN",
        "births_per_100k": 2
      },
      {
        "state": "IL",
        "births_per_100k": 2
      },
      {
        "state": "MD",
        "births_per_100k": 2
      },
      {
        "state": "KY",
        "births_per_100k": 2
      },
      {
        "state": "WA",
        "births_per_100k": 1
      },
      {
        "state": "NY",
        "births_per_100k": 1
      },
      {
        "state": "IN",
        "births_per_100k": 1
      },
      {
        "state": "CA",
        "births_per_100k": 1
      },
      {
        "state": "MN",
        "births_per_100k": 1
      },
      {
        "state": "FL",
        "births_per_100k": 0
      }
    ],
    "decade_graph": [
      {
        "decade": 1970,
        "births_per_100k": 5,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 4,
        "baby_gender": "Female"
      },
      {
        "decade": 1990,
        "births_per_100k": 3,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 0,
        "baby_gender": "Female"
      }
    ]
  }
]
WITH __stage0 AS (
  SELECT
    group_set,
    (floor(base.year/10))*10 as decade__0,
    base.state as state__0,
    CASE WHEN base.gender='M' THEN 'Male' ELSE 'Female' END as baby_gender__0,
    base.gender as gender__0,
    CASE WHEN group_set=0 THEN SUM(base.number) END as cohort_population__0,
    CASE WHEN group_set=1 THEN base.name END as name__1,
    CASE WHEN group_set=1 THEN SUM(base.number) END as population__1
  FROM `bigquery-public-data.usa_names.usa_1910_current` as base
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,1,1)))
  GROUP BY 1,2,3,4,5,7
)
, __stage1 AS (
  SELECT
    decade__0 as decade,
    state__0 as state,
    baby_gender__0 as baby_gender,
    gender__0 as gender,
    ANY_VALUE(CASE WHEN group_set=0 THEN cohort_population__0 END) as cohort_population,
    ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(name__1 as name, population__1 as population) END IGNORE NULLS  ORDER BY  population__1 desc ) as by_name
  FROM __stage0
  GROUP BY 1,2,3,4
  ORDER BY 5 desc
)
, __stage2 AS (
  SELECT
    group_set,
    concat(by_name_0.name,'') as name__0,
    CASE WHEN group_set=0 THEN SUM(by_name_0.population) END as total_population__0,
    CASE WHEN group_set=1 THEN names.state END as state__1,
    CASE WHEN group_set=1 THEN floor((SUM(by_name_0.population)/CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64))*100000) END as births_per_100k__1,
    CASE WHEN group_set=2 THEN names.decade END as decade__2,
    CASE WHEN group_set=2 THEN floor((SUM(by_name_0.population)/CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64))*100000) END as births_per_100k__2,
    CASE WHEN group_set=2 THEN names.baby_gender END as baby_gender__2
  FROM (SELECT GENERATE_UUID() as __distinct_key, * FROM __stage1) as names
  , UNNEST(names.by_name) as by_name_0
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,2,1)))
  WHERE concat(by_name_0.name,'') LIKE 'Mich%'
  GROUP BY 1,2,4,6,8
)
SELECT
  name__0 as name,
  ANY_VALUE(CASE WHEN group_set=0 THEN total_population__0 END) as total_population,
  ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(state__1 as state, births_per_100k__1 as births_per_100k) END IGNORE NULLS  ORDER BY  births_per_100k__1 desc ) as state_map,
  ARRAY_AGG(CASE WHEN group_set=2 THEN STRUCT(decade__2 as decade, births_per_100k__2 as births_per_100k, baby_gender__2 as baby_gender) END IGNORE NULLS  ORDER BY  births_per_100k__2 desc ) as decade_graph
FROM __stage2
GROUP BY 1
ORDER BY 2 desc
LIMIT 15

Iconic Names by State and Gender

Calculate the births per 100K for a name in general and a name within a state. Compute and sort by a ratio to figure out relative popularity.

query: names { where: decade < 1970 } -> {
  group_by: name
  group_by: gender
  aggregate: births_per_100k
  nest: by_state is {
    group_by: state
    aggregate: births_per_100k
  }
} -> {
  where: births_per_100k > 50
  group_by: by_state.state
  nest: by_gender is {
    group_by: gender
    nest: by_name is {
      top: 15
      order_by: popularity desc
      group_by: name
      aggregate: popularity is (by_state.births_per_100k - births_per_100k) / births_per_100k
    }
  }
}
QUERY RESULTS
state by_​gender
AK
gender
F
by_name
name popularity
Daisy 5.229
Shannon 2.506
Lucy 2.389
Sophie 1.918
Agnes 1.852
Tina 1.846
Deanna 1.841
Katherine 1.735
Colleen 1.652
Kristin 1.567
Leslie 1.497
Teresa 1.467
Heather 1.424
Heidi 1.364
Valerie 1.361
gender
M
by_name
name popularity
Shawn 3.153
Jason 2.708
Troy 1.667
Tracy 1.623
Sean 1.605
Peter 1.521
Patrick 1.444
Karl 1.405
Matthew 1.311
Dwayne 1.281
Darren 1.227
Eric 1.206
Mike 1.148
Andrew 1.14
Micheal 1.128
AL
gender
F
by_name
name popularity
Annie 4.751
Willie 3.772
Mattie 3.689
Johnnie 3.338
Rosie 3.034
Shelia 2.987
Katie 2.964
Fannie 2.84
Lillie 2.705
Essie 2.627
Lula 2.536
Minnie 2.412
Hattie 2.318
Mamie 2.301
Susie 2.299
gender
M
by_name
name popularity
Willie 4.273
Charlie 3.119
Roosevelt 2.824
Earnest 2.691
Jessie 2.576
Johnnie 2.554
Rickey 2.33
Eddie 2.284
Jimmie 2.24
Horace 2.129
Otis 2.1
Jimmy 2.036
Johnny 1.912
Billy 1.825
Bennie 1.793
AR
gender
F
by_name
name popularity
Rosie 3.23
Bobbie 2.79
Erma 2.773
Patsy 2.558
Billie 2.486
Ruby 2.28
Velma 2.245
Opal 2.216
Ollie 2.196
Geneva 2.163
Willie 2.117
Essie 1.941
Jessie 1.883
Shelia 1.848
Lola 1.838
gender
M
by_name
name popularity
Rickey 3.636
Billy 3.309
Bobby 3.242
Jimmy 2.891
Jessie 2.455
Earnest 2.426
Freddie 2.321
Tommy 2.281
Jimmie 2.202
Otis 2.15
Jackie 2.138
Bennie 1.897
Roosevelt 1.882
Luther 1.821
Cecil 1.748
AZ
gender
F
by_name
name popularity
Carmen 5.642
Cecilia 3.719
Maria 3.649
Lupe 3.222
Lydia 3.197
Yolanda 3
Ramona 2.556
Dora 1.956
Guadalupe 1.911
Susie 1.61
Alicia 1.606
Veronica 1.504
Christina 1.492
Rosie 1.471
Monica 1.394
gender
M
by_name
name popularity
Manuel 8.991
Ruben 5.639
Francisco 5
Carlos 4.418
Jesus 4.324
Jose 3.604
Gilbert 3.48
Antonio 3.476
Raul 3.145
Alex 3.078
Hector 2.962
Miguel 2.722
Juan 2.457
Luis 2.365
Woodrow 2.058
CA
gender
F
by_name
name popularity
Christina 1.358
Carmen 1.347
Yolanda 1.257
Teri 1.06
Maria 1.009
Pat 0.907
Cecilia 0.877
Leslie 0.848
Julie 0.837
Victoria 0.829
Monica 0.78
Ramona 0.764
Alicia 0.727
Lydia 0.724
Shannon 0.718
gender
M
by_name
name popularity
Manuel 2.327
Ruben 2
Carlos 1.468
Mario 1.231
Mike 1.196
Jason 1.069
Bob 1.036
Lance 0.982
Gilbert 0.967
Jim 0.965
Dave 0.91
Ricardo 0.873
Jose 0.86
Eric 0.829
Chris 0.824
CO
gender
F
by_name
name popularity
Kristi 1.962
Shelly 1.368
Olive 1.269
Bernadette 1.211
Ramona 1.194
Becky 1.101
Nicole 1.091
Heather 1.071
Della 1.053
Heidi 1.03
Erma 1.015
Dianna 1.015
Karla 0.968
Kelli 0.964
Shannon 0.918
gender
M
by_name
name popularity
Kent 1.944
Manuel 1.636
Kirk 1.606
Woodrow 1.558
Gilbert 1.374
Kelly 1.333
Brett 1.288
Bob 1.205
Shawn 1.119
Delbert 1.109
Rick 1.104
Mike 1.07
Jason 1.028
Dave 1
Joe 0.985
CT
gender
F
by_name
name popularity
Sophie 2.656
Doreen 2.646
Claire 1.772
Lynn 1.664
Gail 1.564
Antoinette 1.513
Kristen 1.509
Maureen 1.477
Maryann 1.461
Laurie 1.408
Marion 1.343
Jennie 1.258
Heather 1.188
Anne 1.143
Heidi 1.101
gender
M
by_name
name popularity
Peter 2.449
Nicholas 1.984
Salvatore 1.9
Jonathan 1.737
Angelo 1.643
Francis 1.475
Alexander 1.353
Vincent 1.269
Marc 1.171
Sean 1.168
Frederick 1.153
Anthony 1.022
Louis 0.966
Joseph 0.908
Theodore 0.841
DC
gender
F
by_name
name popularity
Cassandra 2.019
Crystal 2.017
Yvette 1.867
Tonya 1.833
Tanya 1.719
Gwendolyn 1.559
Leslie 1.4
Jacqueline 1.365
Delores 1.331
Laverne 1.327
Renee 1.319
Vanessa 1.316
Nicole 1.291
Monica 1.266
Yvonne 1.253
gender
M
by_name
name popularity
Tyrone 5.263
Reginald 3.785
Derrick 2.627
Maurice 1.908
Ricardo 1.764
Darryl 1.693
Dwayne 1.688
Derek 1.685
Nathaniel 1.417
Jonathan 1.331
Sean 1.261
Eric 1.219
Clifton 1.167
Bernard 1.093
Christopher 1
DE
gender
F
by_name
name popularity
Crystal 3.397
Sophie 2.869
Mabel 2.578
Stella 2.339
Hilda 2.325
Heather 2.129
Elsie 2.097
Pearl 1.839
Nicole 1.836
Agnes 1.698
Holly 1.589
Kristin 1.582
Dawn 1.519
Olive 1.519
Blanche 1.449
gender
M
by_name
name popularity
Woodrow 3.981
Francis 2.381
Sean 1.479
Matthew 1.257
Tyrone 1.246
Harry 1.212
Roland 1.168
Christopher 1.167
Franklin 1.139
Dwayne 1.063
Horace 1.048
Willard 1.047
Wayne 1.04
Bryan 1.034
Jonathan 0.907
FL
gender
F
by_name
name popularity
Cassandra 1.704
Gwendolyn 1.525
Jacquelyn 1.323
Rosa 1.043
Essie 0.98
Belinda 0.967
Ernestine 0.909
Annie 0.866
Iris 0.839
Tina 0.829
Daisy 0.829
Johnnie 0.824
Angela 0.821
Willie 0.772
Inez 0.743
gender
M
by_name
name popularity
Nathaniel 2.236
Freddie 1.744
Willie 1.716
Johnnie 1.438
Reginald 1.367
Roosevelt 1.235
Horace 1.177
Eddie 1.096
Charlie 0.902
Derrick 0.896
Jimmie 0.845
Johnny 0.843
Dwayne 0.766
Tyrone 0.754
Earnest 0.75
GA
gender
F
by_name
name popularity
Annie 3.997
Mattie 3.763
Willie 3.178
Essie 3.098
Mamie 3.027
Johnnie 2.73
Susie 2.494
Fannie 2.444
Lillie 2.409
Hattie 2.282
Katie 2.25
Sara 2.183
Rosa 2.173
Daisy 2.071
Lucile 2.068
gender
M
by_name
name popularity
Charlie 4.636
Willie 4.57
Horace 3.081
Otis 2.733
Bennie 2.483
Johnnie 2.438
Eddie 2.391
Freddie 2.115
Nathaniel 2.083
Earnest 2.074
Johnny 2.044
Marion 1.813
Roosevelt 1.804
Hugh 1.772
Hubert 1.769
HI
gender
F
by_name
name popularity
Kiyoko 6.789
Yoshiko 6.393
Masako 5.984
Fumiko 5.911
Mitsue 5.868
Shizue 4.775
Roxanne 4.723
Bernadette 4.579
Yoshie 4.027
Shizuko 3.975
Kikue 3.966
Iris 3.911
Mitsuko 3.888
Violet 3.632
Doreen 3.415
gender
M
by_name
name popularity
Toshio 7.902
Yoshio 7.885
Kiyoshi 6.985
Kazuo 6.426
Lance 6.089
Minoru 5.981
Derek 5.833
Takeo 4.941
Jason 4.792
Clayton 4.373
Shigeo 4.311
Nathan 3.968
Guy 3.588
Wendell 3.208
Sean 3.092
IA
gender
F
by_name
name popularity
Becky 1.927
Karla 1.921
Maxine 1.826
Shelly 1.821
Kristi 1.577
Julie 1.523
Jodi 1.443
Sheryl 1.403
Marcella 1.339
Connie 1.327
Tami 1.302
Erma 1.288
Jody 1.269
Kristine 1.227
Roxanne 1.108
gender
M
by_name
name popularity
Lyle 3.762
Duane 2.508
Kent 2.113
Dean 2.096
Woodrow 1.788
Darin 1.774
Max 1.685
Bradley 1.61
Rick 1.584
Delbert 1.564
Virgil 1.487
Dale 1.471
Lynn 1.45
Jon 1.441
Rex 1.439
ID
gender
F
by_name
name popularity
Olive 3.808
Karla 3.206
Heather 2.929
Shannon 2.812
Shelley 2.73
Kristi 2.558
Maxine 2.53
Becky 2.468
Blanche 2.359
Tamara 2.342
Robyn 2.17
Tami 2.159
Jody 2.119
Opal 2.093
Shelly 2.019
gender
M
by_name
name popularity
Kelly 5.944
Brent 5.598
Darin 5.453
Lynn 5.2
Woodrow 4.231
Kim 3.879
Kent 3.775
Lyle 3.413
Rex 3.368
Max 2.973
Tracy 2.803
Brett 2.493
Brad 2.462
Delbert 2.4
Rick 2.264
IL
gender
F
by_name
name popularity
Laverne 1.273
Dolores 0.667
Rosemary 0.612
Lorraine 0.606
Genevieve 0.604
Marcella 0.581
Pat 0.556
Marianne 0.5
Marilyn 0.459
Judith 0.416
Loretta 0.383
Arlene 0.37
Eileen 0.354
Bernice 0.348
Kristine 0.33
gender
M
by_name
name popularity
Jerome 0.604
Derrick 0.537
Tyrone 0.474
Maurice 0.429
Bradley 0.366
Tom 0.336
Lyle 0.333
Brad 0.295
Chester 0.273
Patrick 0.264
Kurt 0.256
Lawrence 0.252
Bob 0.25
Martin 0.248
Darryl 0.248
IN
gender
F
by_name
name popularity
Tamara 1.351
Patty 1.193
Tina 1.146
Wilma 1.017
Penny 0.969
Beth 0.96
Marsha 0.952
Olive 0.942
Sue 0.937
Lana 0.922
Dianna 0.912
Becky 0.826
Tonya 0.822
Marcia 0.814
Rebecca 0.755
gender
M
by_name
name popularity
Max 2.37
Rex 1.93
Kent 1.338
Rick 1.28
Brent 1.134
Bradley 1.033
Jeffery 0.967
Jeff 0.954
Tim 0.946
Wilbur 0.905
Darrin 0.885
Kerry 0.879
Darin 0.868
Garry 0.833
Terry 0.754
KS
gender
F
by_name
name popularity
Karla 2.111
Kristi 1.923
Maxine 1.811
Shelly 1.792
Wilma 1.73
Opal 1.557
Deanna 1.449
Connie 1.414
Velma 1.392
Lana 1.373
Lola 1.294
Dana 1.259
Beulah 1.233
Dianna 1.221
Sheryl 1.202
gender
M
by_name
name popularity
Darin 3.528
Max 2.452
Rex 2.281
Delbert 2.2
Kelly 2.167
Virgil 2.013
Lyle 1.984
Kent 1.873
Bradley 1.634
Brad 1.526
Bob 1.375
Brent 1.329
Duane 1.234
Everett 1.231
Marion 1.213
KY
gender
F
by_name
name popularity
Geneva 2.827
Della 2.07
Opal 1.969
Shelia 1.797
Wilma 1.759
Beulah 1.616
Ruby 1.472
Wanda 1.425
Vickie 1.357
Ada 1.348
Alma 1.273
Nora 1.2
Myrtle 1.183
Thelma 1.103
Eula 1.097
gender
M
by_name
name popularity
Donnie 3.343
Jackie 2.255
Wendell 1.792
Homer 1.729
Virgil 1.718
Cecil 1.687
Ronnie 1.646
Bobby 1.596
Billy 1.596
Hubert 1.538
Garry 1.5
Everett 1.462
Danny 1.368
Willard 1.365
Luther 1.328
LA
gender
F
by_name
name popularity
Shelia 2.582
Irma 1.824
Eula 1.758
Rosie 1.713
Vanessa 1.663
Gwendolyn 1.619
Mable 1.581
Lillie 1.43
Hilda 1.373
Bobbie 1.31
Glenda 1.306
Ella 1.302
Willie 1.284
Cassandra 1.241
Paulette 1.217
gender
M
by_name
name popularity
Sidney 2.385
Clifton 2.25
Freddie 2.231
Alvin 2.071
Roosevelt 1.98
Jessie 1.909
Dwayne 1.719
Willie 1.635
Tyrone 1.491
Oliver 1.49
Kerry 1.466
Herman 1.43
Earnest 1.412
Calvin 1.401
Byron 1.358
MA
gender
F
by_name
name popularity
Claire 5.013
Maureen 3.052
Priscilla 2.288
Lynne 1.826
Jeannette 1.779
Anne 1.585
Joanne 1.524
Doreen 1.477
Eleanor 1.475
Muriel 1.418
Sophie 1.393
Marion 1.295
Eileen 1.282
Jeanne 1.277
Lorraine 1.236
gender
M
by_name
name popularity
Francis 2.843
Peter 2.057
Gerard 2.014
Dana 1.87
Roland 1.642
Leo 1.503
Frederick 1.281
Stephen 1.266
Philip 1.186
Paul 1.179
Jonathan 1.144
Alfred 1.028
Sean 0.966
Arthur 0.903
Joseph 0.856
MD
gender
F
by_name
name popularity
Crystal 1.638
Hilda 1.325
Vanessa 1.306
Yvette 1.217
Robin 1.016
Rosalie 0.887
Darlene 0.885
Bernadette 0.825
Dawn 0.814
Terry 0.803
Catherine 0.803
Tracey 0.761
Regina 0.75
Naomi 0.75
Michele 0.749
gender
M
by_name
name popularity
Tyrone 2.07
Dwayne 1.484
Roland 1.221
Reginald 1.127
Clifton 1.117
Bernard 1.07
Maurice 0.969
Franklin 0.869
Melvin 0.861
Milton 0.86
Vernon 0.84
Jerome 0.832
Harry 0.803
Nelson 0.732
Darrin 0.731
ME
gender
F
by_name
name popularity
Priscilla 4.3
Jeannette 3.632
Muriel 3.313
Olive 2.654
Claire 2.506
Constance 2.305
Pauline 2.265
Madeline 2.067
Laurie 1.937
Rachel 1.85
Debora 1.667
Kristen 1.623
Marguerite 1.621
Heidi 1.576
Tina 1.529
gender
M
by_name
name popularity
Dana 7.278
Roland 6.516
Woodrow 4.308
Gerard 3.722
Maurice 3.633
Clayton 2.864
Reginald 2.62
Everett 2.359
Troy 2.141
Philip 2.01
Adam 1.963
Normand 1.852
Marc 1.714
Leo 1.693
Shawn 1.619
MI
gender
F
by_name
name popularity
Sally 0.76
Dawn 0.74
Colleen 0.652
Sue 0.626
Julie 0.616
Marcia 0.603
Kay 0.579
Genevieve 0.56
Kelly 0.556
Suzanne 0.528
Marlene 0.513
Michelle 0.491
Renee 0.488
Sheryl 0.476
Shelly 0.462
gender
M
by_name
name popularity
Duane 1.065
Gerald 0.939
Kim 0.862
Gordon 0.855
Dave 0.836
Lyle 0.825
Rick 0.784
Darrin 0.731
Dale 0.692
Fredrick 0.673
Kurt 0.616
Kirk 0.606
Douglas 0.556
Dennis 0.534
Tim 0.514
MN
gender
F
by_name
name popularity
Jodi 2.545
Jody 2.06
Marcella 2
Kristin 1.881
Roxanne 1.862
Kristine 1.716
Kristi 1.712
Colleen 1.553
Harriet 1.542
Julie 1.472
Lorraine 1.407
Heidi 1.394
Delores 1.324
Shelly 1.321
Marlene 1.256
gender
M
by_name
name popularity
Lyle 3.079
Duane 2.919
Bradley 2.675
Jerome 1.96
Dean 1.955
Jason 1.889
Darin 1.642
Gordon 1.62
Todd 1.536
Jon 1.469
Troy 1.43
Dale 1.399
Arnold 1.366
Allan 1.294
Scott 1.268
MO
gender
F
by_name
name popularity
Wilma 1.471
Opal 1.155
Laverne 1.073
Velma 0.931
Marcella 0.855
Lana 0.824
Christy 0.804
Beulah 0.756
Carla 0.745
Shelly 0.736
Glenda 0.729
Georgia 0.726
Norma 0.717
Erma 0.712
Marsha 0.644
gender
M
by_name
name popularity
Virgil 1.397
Darin 1.113
Bob 1.045
Jackie 1.021
Everett 0.923
Delbert 0.909
Bill 0.847
Elmer 0.803
Garry 0.7
Vernon 0.686
Lloyd 0.676
Floyd 0.673
Oliver 0.647
Billy 0.641
Marion 0.6
MS
gender
F
by_name
name popularity
Rosie 8.402
Annie 5.016
Willie 4.904
Bobbie 4.2
Maggie 4.078
Mattie 4.052
Katie 4.036
Lillie 4
Hattie 3.988
Essie 3.902
Johnnie 3.824
Lula 3.738
Fannie 3.42
Minnie 3.176
Jessie 3.039
gender
M
by_name
name popularity
Willie 6.282
Roosevelt 6.078
Jessie 5.535
Johnnie 4.339
Eddie 3.863
Earnest 3.794
Jimmie 3.744
Charlie 3.734
Bennie 3.569
Clifton 2.85
Otis 2.8
Luther 2.373
Sam 2.349
Freddie 2.192
Johnny 1.96
MT
gender
F
by_name
name popularity
Tami 2.683
Mabel 2.667
Pat 2.593
Jamie 2.521
Shelly 2.5
Teri 2.418
Olive 2.385
Karla 2.302
Kristi 2.192
Patti 2.119
Genevieve 2.066
Colleen 1.929
Shannon 1.906
Shelley 1.899
Marlene 1.776
gender
M
by_name
name popularity
Kelly 3
Lyle 2.714
Shawn 2.407
Woodrow 2.288
Brett 2.123
Jason 1.972
Brad 1.962
Darin 1.925
Duane 1.887
Darren 1.814
Kirk 1.761
Dan 1.722
Tim 1.676
Lance 1.625
Rick 1.608
NC
gender
F
by_name
name popularity
Annie 3.016
Maggie 2.313
Katie 2.143
Hattie 2.129
Mamie 1.973
Sadie 1.893
Mable 1.71
Lillie 1.604
Shelia 1.557
Rosa 1.554
Fannie 1.469
Hilda 1.458
Rachel 1.425
Crystal 1.414
Daisy 1.4
gender
M
by_name
name popularity
Charlie 2.713
Clifton 2.2
Bobby 2.021
Hubert 1.969
Luther 1.955
Johnnie 1.688
Donnie 1.687
Horace 1.645
Clyde 1.424
Claude 1.418
Willie 1.374
Johnny 1.372
Ricky 1.286
Jimmy 1.258
Marshall 1.197
ND
gender
F
by_name
name popularity
Kristi 6.346
Karla 5.429
Violet 4.057
Myrtle 3.603
Shelly 3.472
Viola 3.297
Roxanne 3.277
Jodi 3.136
Marlene 3.135
Paulette 3.043
Olga 2.853
Hilda 2.831
Olive 2.692
Leona 2.642
Delores 2.615
gender
M
by_name
name popularity
Lyle 6.333
Darin 6.132
Troy 5.985
Duane 5.839
Kelly 3.611
Brent 3.305
Darren 3.206
Bradley 3
Lynn 2.967
Jason 2.819
Arnold 2.554
Gordon 2.53
Vernon 2.526
Kim 2.466
Dean 2.436
NE
gender
F
by_name
name popularity
Kristi 3.192
Opal 2.742
Erma 2.197
Maxine 2.023
Shelly 1.934
Jody 1.522
Tami 1.492
Jodi 1.409
Julie 1.408
Olive 1.404
Leona 1.377
Wilma 1.356
Kay 1.306
Deanna 1.243
Christy 1.235
gender
M
by_name
name popularity
Lyle 4.508
Duane 2.46
Darin 2.377
Kent 2.141
Dean 1.814
Dale 1.805
Bradley 1.805
Delbert 1.709
Rex 1.649
Virgil 1.628
Troy 1.607
Lynn 1.55
Kirk 1.493
Rick 1.408
Kelly 1.403
NH
gender
F
by_name
name popularity
Kristen 6.698
Claire 6.203
Jeannette 4.912
Priscilla 4.863
Heidi 3.889
Blanche 3.808
Muriel 3.612
Olive 3.385
Pauline 3.313
Nicole 2.745
Kristin 2.597
Sophie 2.508
Stella 2.492
Lorraine 2.175
Gertrude 2.155
gender
M
by_name
name popularity
Roland 7.726
Maurice 6.01
Gerard 5.139
Dana 4.426
Marc 3.181
Leo 2.687
Normand 2.309
Shawn 2.153
Jonathan 2.136
Peter 1.978
Norman 1.954
Roger 1.828
Jason 1.611
Bruce 1.494
Scott 1.345
NJ
gender
F
by_name
name popularity
Maryann 2.289
Claire 1.873
Eileen 1.578
Antoinette 1.526
Angelina 1.462
Rosemarie 1.418
Madeline 1.28
Maureen 1.267
Marianne 1.214
Michele 1.148
Doreen 1.138
Joan 1.03
Eleanor 0.993
Marion 0.971
Sophie 0.967
gender
M
by_name
name popularity
Nicholas 2.146
Dominick 1.961
Angelo 1.911
Vincent 1.671
Peter 1.492
Alexander 1.294
Salvatore 1.289
Anthony 1.23
Gerard 1.167
Joseph 1.147
Frank 0.95
Louis 0.931
Frederick 0.803
Edward 0.785
Theodore 0.785
NM
gender
F
by_name
name popularity
Cecilia 6
Ramona 4.694
Carmen 4.421
Bernadette 3.965
Maria 3.782
Lucy 3.595
Priscilla 3.563
Flora 2.969
Yolanda 2.771
Dora 2.67
Marcella 2.645
Lydia 2.421
Lupe 2.222
Stella 2.161
Rosie 2.115
gender
M
by_name
name popularity
Manuel 10.227
Jose 7.425
Antonio 5.476
Gilbert 5.228
Ruben 5.016
Juan 4.843
Carlos 4.278
Joe 3.835
Pedro 2.887
Alex 2.766
Luis 2.765
Francisco 2.75
Miguel 2.741
Jesus 2.676
Bennie 2.517
NV
gender
F
by_name
name popularity
Olga 5.103
Traci 4.339
Shannon 4.2
Stacey 4.148
Pat 3.981
Toni 3.165
Natalie 3.15
Teri 3.015
Kelli 2.982
Marguerite 2.947
Tina 2.888
Blanche 2.795
Tami 2.746
Nicole 2.745
Leslie 2.673
gender
M
by_name
name popularity
Jason 4.333
Shawn 4.178
Troy 4.022
Kelly 3.736
Woodrow 3.654
Lance 2.982
Darren 2.711
Mike 2.526
Brent 2
Adam 2
Aaron 1.986
Darin 1.962
Brett 1.959
Delbert 1.891
Tracy 1.639
NY
gender
F
by_name
name popularity
Antoinette 1.756
Eileen 1.675
Madeline 1.653
Miriam 1.618
Muriel 1.567
Angelina 1.5
Rosemarie 1.463
Maureen 1.355
Maryann 1.158
Marion 1
Josephine 1
Claire 0.987
Anne 0.987
Joan 0.915
Doreen 0.908
gender
M
by_name
name popularity
Salvatore 2.222
Angelo 2.054
Dominick 2.039
Vincent 1.963
Nicholas 1.797
Gerard 1.792
Peter 1.687
Stuart 1.21
Anthony 1.184
Seymour 1.111
Alexander 1.035
Martin 0.915
Neil 0.908
Philip 0.89
Louis 0.849
OH
gender
F
by_name
name popularity
Beth 0.678
Patty 0.651
Sue 0.638
Amy 0.591
Marcella 0.5
Dianna 0.456
Becky 0.45
Tonya 0.433
Kimberly 0.422
Rosemary 0.412
Marcia 0.397
Darla 0.397
Penny 0.394
Sandy 0.392
Marilyn 0.39
gender
M
by_name
name popularity
Rick 0.776
Tim 0.703
Dave 0.687
Dale 0.597
Wilbur 0.587
Darrin 0.519
Jeffrey 0.499
Tom 0.493
Timothy 0.481
Jeffery 0.475
Jeff 0.424
Ted 0.41
Karl 0.369
Carl 0.356
Dwight 0.351
OK
gender
F
by_name
name popularity
Opal 4.134
Billie 3.706
Shelly 2.396
Velma 2.196
Kristi 2.192
Patsy 2.154
Darla 2.138
Wanda 2.114
Wilma 1.931
Geneva 1.786
Lola 1.765
Juanita 1.704
Erma 1.682
Kelli 1.655
Ruby 1.609
gender
M
by_name
name popularity
Billy 3.137
Jackie 2.713
Jimmie 2.543
Jimmy 2.367
Tommy 2.292
Bill 2.243
Cecil 2.148
Bobby 2.11
Delbert 2.036
Bob 1.964
Virgil 1.833
Floyd 1.752
Rickey 1.67
Don 1.535
Ronnie 1.472
OR
gender
F
by_name
name popularity
Tami 3.095
Shannon 2.647
Shelley 1.944
Julie 1.912
Kristi 1.904
Tamara 1.883
Lucile 1.695
Shelly 1.679
Jan 1.557
Becky 1.477
Deanna 1.449
Kelli 1.436
Teri 1.433
Karla 1.413
Traci 1.371
gender
M
by_name
name popularity
Darin 2.981
Woodrow 2.75
Kelly 2.569
Rick 2.16
Jason 2.028
Jim 1.849
Bob 1.786
Dan 1.722
Dave 1.657
Brad 1.603
Delbert 1.6
Jeff 1.583
Mike 1.561
Lyle 1.524
Bradley 1.512
PA
gender
F
by_name
name popularity
Anna 1.388
Dolores 1.13
Maryann 1.053
Eleanor 0.979
Miriam 0.974
Bernadette 0.965
Marian 0.748
Sophie 0.738
Rosemarie 0.716
Agnes 0.711
Helen 0.692
Florence 0.687
Stella 0.627
Joanne 0.622
Marianne 0.6
gender
M
by_name
name popularity
Francis 1.395
Harry 1.147
Joseph 1.013
Nicholas 0.919
Barry 0.713
Bernard 0.668
Vincent 0.598
Edward 0.595
Andrew 0.556
Samuel 0.552
Theodore 0.528
Alexander 0.482
Albert 0.463
Stanley 0.452
Frank 0.441
RI
gender
F
by_name
name popularity
Claire 7.051
Kristen 4.245
Muriel 4.164
Angelina 4
Doreen 3.4
Maureen 3.221
Jeannette 3.015
Madeline 2.48
Flora 2.313
Jennie 2.289
Gertrude 2.208
Laurie 2.103
Gail 2.011
Olga 1.956
Sophie 1.951
gender
M
by_name
name popularity
Roland 4.263
Gerard 3.167
Normand 2.901
Angelo 2.357
Leo 2.307
Marc 2.276
Alfred 1.968
Peter 1.891
Manuel 1.818
Raymond 1.765
Francis 1.642
Maurice 1.551
Vincent 1.521
Antonio 1.476
Anthony 1.322
SC
gender
F
by_name
name popularity
Hattie 4.224
Rosa 4.094
Maggie 3.984
Janie 3.949
Annie 3.816
Mamie 3.753
Daisy 3.543
Lillie 3.517
Sadie 3.125
Carrie 3.114
Essie 2.804
Lula 2.69
Mattie 2.63
Willie 2.406
Jessie 2.266
gender
M
by_name
name popularity
Marion 4.875
Nathaniel 4.375
Willie 3.948
Roosevelt 3.333
Woodrow 2.827
Luther 2.716
Johnnie 2.589
Julius 2.571
Leroy 2.512
Charlie 2.406
Bennie 2.345
Freddie 2.154
Otis 2.133
Horace 2.097
Ben 1.761
SD
gender
F
by_name
name popularity
Kristi 4.404
Karla 3.937
Darla 3.362
Tamara 2.775
Jody 2.612
Violet 2.566
Mabel 2.559
Kay 2.521
Delores 2.439
Maxine 2.348
Verna 2.284
Viola 2.281
Olive 2.173
Leona 2.17
Lana 2.078
gender
M
by_name
name popularity
Lyle 7.286
Darin 4.962
Kelly 4.236
Duane 4.089
Bradley 3.211
Brad 2.628
Troy 2.556
Todd 2.431
Delbert 2.364
Dean 2.301
Dale 2.231
Elmer 1.909
Daryl 1.897
Kim 1.862
Virgil 1.859
TN
gender
F
by_name
name popularity
Shelia 2.772
Bobbie 1.82
Wilma 1.569
Ruby 1.562
Patsy 1.538
Billie 1.477
Mattie 1.452
Glenda 1.403
Geneva 1.398
Willie 1.391
Maggie 1.375
Melissa 1.251
Wanda 1.229
Tonya 1.211
Johnnie 1.203
gender
M
by_name
name popularity
Billy 2.384
Jackie 2.266
Bobby 2.217
Rickey 2.011
Donnie 1.97
Jimmy 1.895
Ronnie 1.722
Hubert 1.6
Luther 1.597
Claude 1.538
Tommy 1.503
Johnny 1.482
Ricky 1.479
Hugh 1.405
Charlie 1.28
TX
gender
F
by_name
name popularity
Maria 5.032
Guadalupe 2.744
Yolanda 2.695
Billie 2.495
Irma 2.365
Rosa 2.338
Alicia 2.303
Janie 2.179
Dora 1.835
Olga 1.779
Bobbie 1.6
Patsy 1.596
Carmen 1.589
Lydia 1.579
Juanita 1.552
gender
M
by_name
name popularity
Juan 5.606
Jose 4.903
Ruben 4.508
Manuel 4.036
Jesus 3.765
Pedro 3.677
Roberto 3.618
Ricardo 3.527
Raul 3.493
Carlos 3.316
Francisco 3.309
Antonio 3.274
Hector 2.528
Guadalupe 2.477
Joe 2.128
UT
gender
F
by_name
name popularity
Robyn 5.604
Sherrie 4.569
Kristine 3.341
Erma 3.182
Natalie 3.167
Becky 3.028
Colleen 3.014
Jan 2.949
Julie 2.747
Jody 2.373
Heidi 2.303
Lucile 2.22
Leslie 2.176
Teri 2.104
Shelly 2.094
gender
M
by_name
name popularity
Brent 10.183
Lynn 9
Kelly 7.111
Kent 7.07
Kim 5.931
Darin 5.528
Woodrow 5.365
Brad 4.5
Max 4.137
Troy 4.03
Rex 3.93
Tracy 3.246
Kerry 3.034
Don 2.979
Jay 2.955
VA
gender
F
by_name
name popularity
Nannie 1.721
Vanessa 1.51
Rosa 1.331
Shelia 1.329
Lucy 1.229
Fannie 1.062
Elsie 1.055
Susie 1.052
Thelma 0.97
Hattie 0.965
Iris 0.964
Nellie 0.962
Angela 0.928
Virginia 0.922
Estelle 0.909
gender
M
by_name
name popularity
Charlie 1.762
Clifton 1.7
Claude 1.626
Lewis 1.472
Luther 1.403
Calvin 1.238
Earnest 1.191
Otis 1.183
Marshall 1.115
Cecil 1.113
Nelson 1.089
Reginald 1.051
Franklin 1.033
Woodrow 1.019
Clarence 0.974
VT
gender
F
by_name
name popularity
Madeline 4.8
Marion 4.135
Laurie 4.013
Blanche 3.936
Claire 3.481
Olive 3
Priscilla 2.913
Heather 2.835
Heidi 2.737
Penny 2.276
Marguerite 2.274
Pauline 2.273
Muriel 2.269
Tina 2.246
Jeannette 2.162
gender
M
by_name
name popularity
Jason 4.139
Reginald 3.696
Maurice 3.694
Roland 3.368
Dana 3.315
Wendell 2.717
Francis 2.635
Bernard 2.495
Gordon 2.458
Everett 2.244
Sean 2.151
Shawn 2.144
Clayton 2
Kim 1.793
Todd 1.763
WA
gender
F
by_name
name popularity
Tami 2.968
Shannon 2.2
Kristi 1.788
Heidi 1.707
Olive 1.673
Shelley 1.663
Kelli 1.655
Colleen 1.645
Kristine 1.5
Teri 1.388
Pat 1.259
Tamara 1.234
Gayle 1.138
Julie 1.133
Deanna 1.121
gender
M
by_name
name popularity
Kelly 2.236
Woodrow 2.192
Rick 2.04
Dave 2
Bob 1.929
Jim 1.849
Dan 1.765
Jason 1.667
Jeff 1.629
Mike 1.57
Brad 1.372
Gordon 1.355
Greg 1.355
Bill 1.28
Jon 1.214
WI
gender
F
by_name
name popularity
Kristine 2.83
Laverne 2.455
Jodi 1.784
Marcella 1.726
Kristin 1.328
Lynn 1.327
Sophie 1.279
Kay 1.223
Julie 1.157
Jody 1.134
Lorraine 1.077
Violet 1.057
Jill 1.034
Arlene 1.012
Audrey 0.946
gender
M
by_name
name popularity
Jerome 2.107
Lyle 2.032
Todd 1.807
Duane 1.637
Dean 1.385
Kurt 1.384
Gordon 1.217
Dale 1.156
Jon 1.131
Allan 1.129
Gerald 1.09
Scott 1.078
Arnold 0.92
Gregg 0.882
Jeffrey 0.806
WV
gender
F
by_name
name popularity
Patty 2.241
Opal 1.969
Dianna 1.779
Beulah 1.779
Violet 1.774
Mabel 1.676
Naomi 1.607
Nellie 1.481
Della 1.386
Tammy 1.354
Wilma 1.339
Sherri 1.151
Nina 1.138
Thelma 1.122
Maxine 1.053
gender
M
by_name
name popularity
Jackie 2.606
Delbert 2.2
Homer 2.043
Franklin 2
Woodrow 1.654
Cecil 1.652
Virgil 1.59
Willard 1.424
Everett 1.282
Dana 1.259
Clyde 1.185
Roger 1.109
Danny 1.036
Luther 1.015
Lewis 1.007
WY
gender
F
by_name
name popularity
Pat 3.074
Shannon 2.894
Becky 2.798
Shelley 2.551
Natalie 2.483
Jody 2.418
Jamie 2.366
Stacy 2.313
Agnes 2.289
Shelly 2.104
Violet 2.094
Olive 2
Laurie 1.982
Hazel 1.974
Opal 1.928
gender
M
by_name
name popularity
Troy 5.941
Jason 4.431
Kelly 3.75
Kim 3.552
Brad 3.192
Kent 2.901
Woodrow 2.596
Brett 2.548
Lance 2.536
Bob 2.277
Darin 2.038
Jim 1.959
Bill 1.693
Darren 1.67
Bradley 1.659 
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]
WITH __stage0 AS (
  SELECT
    group_set,
    (floor(base.year/10))*10 as decade__0,
    base.state as state__0,
    CASE WHEN base.gender='M' THEN 'Male' ELSE 'Female' END as baby_gender__0,
    base.gender as gender__0,
    CASE WHEN group_set=0 THEN SUM(base.number) END as cohort_population__0,
    CASE WHEN group_set=1 THEN base.name END as name__1,
    CASE WHEN group_set=1 THEN SUM(base.number) END as population__1
  FROM `bigquery-public-data.usa_names.usa_1910_current` as base
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,1,1)))
  GROUP BY 1,2,3,4,5,7
)
, __stage1 AS (
  SELECT
    decade__0 as decade,
    state__0 as state,
    baby_gender__0 as baby_gender,
    gender__0 as gender,
    ANY_VALUE(CASE WHEN group_set=0 THEN cohort_population__0 END) as cohort_population,
    ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(name__1 as name, population__1 as population) END IGNORE NULLS  ORDER BY  population__1 desc ) as by_name
  FROM __stage0
  GROUP BY 1,2,3,4
  ORDER BY 5 desc
)
, __stage2 AS (
  SELECT
    group_set,
    concat(by_name_0.name,'') as name__0,
    names.gender as gender__0,
    CASE WHEN group_set=0 THEN floor((SUM(by_name_0.population)/CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64))*100000) END as births_per_100k__0,
    CASE WHEN group_set=1 THEN names.state END as state__1,
    CASE WHEN group_set=1 THEN floor((SUM(by_name_0.population)/CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64))*100000) END as births_per_100k__1
  FROM (SELECT GENERATE_UUID() as __distinct_key, * FROM __stage1) as names
  , UNNEST(names.by_name) as by_name_0
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,1,1)))
  WHERE names.decade<1970
  GROUP BY 1,2,3,5
)
, __stage3 AS (
  SELECT
    name__0 as name,
    gender__0 as gender,
    ANY_VALUE(CASE WHEN group_set=0 THEN births_per_100k__0 END) as births_per_100k,
    ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(state__1 as state, births_per_100k__1 as births_per_100k) END IGNORE NULLS  ORDER BY  births_per_100k__1 desc ) as by_state
  FROM __stage2
  GROUP BY 1,2
  ORDER BY 3 desc
)
, __stage4 AS (
  SELECT
    group_set,
    by_state_0.state as state__0,
    CASE WHEN group_set IN (1,2) THEN base.gender END as gender__1,
    CASE WHEN group_set=2 THEN base.name END as name__2,
    CASE WHEN group_set=2 THEN ((by_state_0.births_per_100k-base.births_per_100k))/base.births_per_100k END as popularity__2
  FROM `__stage3` as base
  , UNNEST(base.by_state) as by_state_0
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,2,1)))
  WHERE base.births_per_100k>50
  GROUP BY 1,2,3,4,5
)
, __stage5 AS (
  SELECT 
    CASE WHEN group_set=2 THEN 1  ELSE group_set END as group_set,
    state__0 as state__0,
    CASE WHEN group_set IN (1,2) THEN gender__1 END as gender__1,
    ARRAY_AGG(CASE WHEN group_set=2 THEN STRUCT(name__2 as name, popularity__2 as popularity) END IGNORE NULLS  ORDER BY  popularity__2 desc  LIMIT 15) as by_name__1
  FROM __stage4
  GROUP BY 1,2,3
)
SELECT
  state__0 as state,
  ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(gender__1 as gender, by_name__1 as by_name) END IGNORE NULLS  ORDER BY  gender__1 asc ) as by_gender
FROM __stage5
GROUP BY 1
ORDER BY 1 asc